CN105996984A - Sedentary Period Detection Using Wearable Electronics - Google Patents

Abstract

A system and method for determining a user's sedentary state is described. Sensor data is collected and analyzed to calculate metabolic equivalents of tasks (METs) measurements for multiple moments of interest. Based on the MET measurements and the time periods during which the MET measurements exceed a threshold, a determination is made as to whether the user is in a sedentary state. If the user is in a sedentary state, a notification is provided to the user to encourage the user to perform non-sedentary activities.

Description

Sedentary Period Detection Using Wearable Electronics

technical field

Embodiments described in this disclosure relate to the field of wearable electronic devices. In particular, embodiments relate to automatic detection of sedentary time periods and promotion of non-sedentary behavior utilizing wearable electronic devices.

Background technique

Trackers have gained popularity among consumers. Trackers are used to track a user's activities using various sensors and help the user maintain a healthy lifestyle. To determine activity, the tracker collects activity data and runs calculations on that data. One difficulty in obtaining an accurate determination of activity is that trackers (as they are worn by the user) typically come in tight packages containing less powerful processors on which it is more difficult to run complex calculate.

Another challenge in tracking activity is the distinction between users who are stationary but performing activities and users who are sedentary (eg, expending little energy, etc.). For example, when a user is sitting or typing on a computer while sitting, the user consumes very little energy. Increased total sedentary time and longer, more sustained periods of sedentary time are associated with poor health and poor health (eg, obesity, metabolic disorders, etc.).

Contents of the invention

In some embodiments, a wearable electronic device to be worn by a user is described. A wearable electronic device contains a collection of one or more sensors to generate sensor data associated with a user when the wearable electronic device is worn by the user. The wearable electronic device also includes a set of one or more processors coupled to the set of sensors and a non-transitory machine-readable storage medium coupled to the set of one or more processors and having instructions stored therein. The instructions, when executed by the set of one or more processors, cause the user's status to be determined to be sedentary for periods of time during tracking by the wearable electronic device. The determination is based on a metabolic equivalent (MET) measurement of the task at the time of interest and the MET measurement is calculated based on the sensor data. The instructions additionally cause the user to receive a notification in response to the tracked time period to encourage the user to limit the length of the sedentary time period.

In various embodiments, the instructions additionally cause the wearable electronic device to classify the user state at each time of interest as sedentary or non-sedentary based on the MET measurements at that time of interest.

In several embodiments, time periods are tracked based on the determination of contiguous moments of interest at which the user's status is classified as sedentary.

In some embodiments, the user state at a moment of interest is classified as sedentary when the MET measurement at that moment of interest is less than a threshold MET value.

In various embodiments, when the MET measurement at a moment of interest is between a first threshold and a second threshold, the user state at that moment of interest is classified as sedentary, and before the moment of interest was A first moment of interest within a threshold window of time when the user's status is sedentary is followed by a second moment of interest within the threshold window of time when the user's status is sedentary.

In some embodiments, one of the one or more sensors is a photoplethysmography (PPG) sensor, and the MET measurement is based on the user's heart rate measurement (computed based on PPG sensor data).

In various embodiments, the instructions cause the wearable electronic device to filter out time periods when the user's state is sleep.

In some embodiments, a wearable electronic device includes sensors to generate sensor data associated with a user when the wearable electronic device is worn by the user. The wearable electronic device also includes a set of one or more processors coupled to the sensors and a non-transitory machine-readable storage medium coupled to the set of one or more processors and having instructions stored therein. The instructions, when executed by the set of one or more processors, cause the wearable electronic device to track periods during which the user's status is determined to be sedentary based on the sensor data. A time period has a start and an end. The instructions additionally cause the wearable electronic device to detect a transition from sedentary to non-sedentary for a threshold period of time in response to the end of the period of time. The instructions cause the wearable device to encourage the user to remain non-sedentary by enabling the user to receive notifications in response to the detection.

In several embodiments, the notification is a message displayed on a display device of the wearable electronic device or a vibration of the wearable electronic device or a sound emitted by the wearable electronic device.

In some embodiments, the notification indicates to the user that the time period during which the user's status was sedentary has ended.

In various embodiments, notifications are determined based on preferences set by the user.

In some embodiments, the notification is an incentive statement displayed on a display device of the wearable electronic device.

In various embodiments, an apparatus for improving the efficiency of notifications provided to a user of a wearable electronic device is described. The apparatus includes an electronic device that includes a sedentary state monitor to notify a user of the wearable electronic device based on a period of time the user is sedentary during tracking to encourage the user to change his/her sedentary behavior. The Sedentary Status Monitor contains a collection of one or more managers to receive current status from the status of multiple users during a period of time. States include sedentary states. The one or more managers cause the wearable electronic device to receive notifications based on the current state to inform the user of the length of time the user has been sedentary during the restriction period.

In some embodiments, the device further includes a sedentary learning unit coupled to receive data regarding notifications from each of the one or more managers. The sedentary learning unit is coupled to the set of one or more sensors of the wearable electronic device to determine which notifications have an effect of modifying the user's sedentary behavior, and to determine an updated configuration of at least one of the one or more managers. The updated configuration improves user response to notifications by limiting the length of time period during which the user is sedentary.

In various embodiments, the one or more managers include a sedentary alert manager, which receives a user's current status over a period of time. The sedentary alert manager generates a sedentary alert based on detection of a time period exceeding a sedentary time period threshold. The sedentary alert manager sends a notification to the wearable electronic device indicating that the time period exceeds the sedentary time period threshold.

In some embodiments, the one or more managers also include a non-sedentary state transition manager to receive the user's current state. The non-sedentary state transition manager generates a notification based on the detection of the end of the sedentary period of the current state. A notification is sent from the non-sedentary state transition manager to the wearable electronic device.

In various embodiments, the sedentary learning unit determines an update of at least one of the sedentary warning manager and the non-sedentary state transition manager based on notification information received from the sedentary warning manager and the non-sedentary state transition manager Configuration. The updated configuration improves the user's response to notification messages to limit the length of the sedentary period during which the user is sedentary.

In some embodiments, the updated configuration includes disabling at least one of the sedentary alarm manager and the non-sedentary state transition manager.

In several embodiments, the sedentary learning unit comprises decision trees, random forests, support vector machines, neural networks, K-nearest neighbors, naive Bayesian or hidden Markov models.

In various embodiments, the sedentary learning unit allows the user to silence alarm notifications.

In some embodiments, the sedentary learning unit determines an updated configuration of at least one of the one or more managers using data related to the notification of the snooze.

In various embodiments, the electronic device is a wearable electronic device.

Description of drawings

The embodiments described in this disclosure are illustrated by way of example and not by way of limitation, like references indicating like elements in the figures of the drawings.

FIG. 1A illustrates sedentary user state detection and sedentary alert management according to various embodiments described in this disclosure.

1B illustrates a flowchart of operations for tracking periods of sedentary time and enabling a user to receive notifications based on periods of sedentary time, according to some embodiments described in this disclosure.

2 illustrates the use of a user's sedentary and non-sedentary states to determine time periods during which the user's state is sedentary, according to several embodiments described in this disclosure.

3 illustrates a sedentary state monitor for informing a user based on tracking of sedentary time periods to encourage the user to change his/her sedentary behavior and limit sedentary time periods, according to some embodiments described in this disclosure. length.

4 illustrates the delivery of a notification to a user based on the detection of the end of a sedentary period and the beginning of a non-sedentary period (a threshold period has been exceeded), according to various embodiments described in this disclosure.

5 illustrates delivery of a sedentary alert to a user based on detection of a sedentary period exceeding a sedentary period threshold, according to some embodiments described in this disclosure.

FIG. 6A illustrates a recording of metabolic equivalent (MET) measurements of a task at successive moments of interest over a period of time and a graph based on the MET measurements at each point of interest, according to various embodiments described in the present disclosure. A classification of the user's state at a moment in time.

FIG. 6B illustrates a recording of MET measurements at consecutive moments of interest over a period of time and a user status at each moment of interest based on the MET measurements, according to some embodiments described in this disclosure. Classification.

7 is a block diagram illustrating a wearable electronic device and an electronic device implementing operations disclosed herein, according to various embodiments described in this disclosure.

8 is a block diagram of a wrist-worn electronic device with buttons, a display, and a wrist strap for securing the wrist-worn electronic device to a user's forearm, according to some embodiments described in this disclosure.

detailed description

In the following description, numerous specific details are set forth. However, it is understood that the embodiments described in this disclosure may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the understanding of the embodiments. However, one skilled in the art will recognize that embodiments may be practiced without such specific details. From the description of the embodiments, one of ordinary skill in the art will be able to implement the appropriate functionality of the embodiments without undue experimentation.

In some embodiments, the terms "coupled" and "connected," along with their derivatives, are used. It should be understood that these terms are not intended as synonyms for each other. For example, "coupled" is used to indicate two or more elements that are in direct or indirect physical or electrical contact, co-operate or interact with each other. Also, in this example, "connected" is used to indicate the establishment of communication between two or more elements coupled to each other. Furthermore, in various embodiments, a "set" as used herein refers to any set of positive integer items, including one item, unless otherwise stated (eg, zero or more).

In some embodiments, the electronic device stores the code internally and/or transmits the code to other electronic devices over a computer network. The code consists of software instructions and is sometimes referred to as computer program code or a computer program stored on a machine-readable storage medium. In some embodiments, the code contains data for the execution of the code. In various embodiments, the machine-readable storage medium is a computer-readable medium. Examples of computer readable media include magnetic disks, optical disks, read only memory (ROM), random access memory (RAM), flash memory devices, phase change memory, and the like. In various embodiments, the code is transmitted using a machine-readable transmission medium (also known as a carrier wave, eg, an electrical, optical, radio, acoustic or other form of propagated signal). Additional examples of carrier waves include carrier waves, infrared signals, and the like.

In various embodiments, an electronic device (eg, computer, etc.) includes hardware and software. For example, an electronic device includes one or more processors coupled to one or more machine-readable storage media that store code for execution on the one or more processors and/or store data. To further illustrate, an electronic device includes non-volatile memory (including code) and the non-volatile memory stores code or data even when the electronic device is turned off (eg, when power to the electronic device is removed, etc.). When the electronic device is turned on, code executed by one or more processors of the electronic device is copied from non-volatile memory to volatile memory (e.g., dynamic random access memory (DRAM), static random access memory (SRAM), etc.). Non-volatile memory is slower to access than volatile memory. Electronic devices also typically contain a collection of one or more network interfaces, such as network interface controllers, network interface cards, Internet access cards, Internet access controllers, etc., each of which establishes a physical or wireless network with other electronic devices Connections are used to transfer code and/or data using propagated signals. A wearable electronic device (WED), described in additional detail below, is an example of an electronic device. It should be noted that one or more portions of the embodiments described in this disclosure may be implemented using different combinations of software, firmware, and/or hardware.

An embodiment describing tracking of a user's sedentary state and generation of sedentary notifications follows.

FIG. 1A illustrates sedentary state detection and sedentary alert management according to some embodiments described in this disclosure. It should be noted that in some embodiments, task blocks 1, 2, 3A, 3B, and 4 of FIG . 1A are performed and components 110, 112, 120, 122, 124, and 126 of FIG. 1A are implemented in a wearable electronic device, or Distributed between the wearable electronic device and one or more of the other electronic devices coupled to the wearable electronic device. In some embodiments, the wearable electronic device is worn on a user's body part, such as an arm, wrist, ankle, or chest, etc., or embedded in clothes worn by the user. Examples of one or more other electronic devices include servers (including hardware and software), tablet computers, smartphones, desktop computers, laptop computers, and smart televisions. In some embodiments, one or more other electronic devices execute applications (sometimes referred to as apps) to implement, for example, sensor data analyzer 112 , user state tracking unit 190 , and/or sedentary notification unit 192 .

Task boxes 1-4 illustrate the order in which components 110, 112, 120, 122, 124, and 126 perform operations. As illustrated in task block 1 , one or more sensors 110 generate sensor data 150 for multiple time intervals. For example, one or more sensors 110 are implemented in a wearable electronic device such that when worn by the user, at least some of the sensor data is indicative of activities performed by the user. Examples of sensor data include biometric data. In some embodiments, one or more sensors 110 that generate sensor data 150 include motion sensors (eg, three-axis accelerometers, etc.). The motion sensor generates motion sensor data indicative of the user's motion (eg, number of steps taken, number of stairs climbed, number of stairs descended, etc.). In various embodiments, the one or more sensors 110 include a heart rate sensor (eg, photoplethysmography (PPG) sensor, etc.) to generate cardiac sensor data (eg, PPG sensor data, etc.) indicative of the user's heart rate. In several embodiments, both the motion sensor and the heart rate sensor are placed in the same wearable electronic device or in different wearable electronic devices. In some embodiments, other types of sensors are placed in the wearable electronic device or devices, for example, gyroscopes, gravity sensors, rotation vector sensors, magnetometers, temperature sensors (which measure the user's skin and/or temperature of the environment surrounding the user), ambient light sensor (measures the ambient light of the environment), galvanic skin response sensor, capacitive sensor, humidity sensor, sound sensor, etc. Examples of the environment surrounding the user include a room where the user is located, a street where the user is standing or driving, the interior of a vehicle where the user is located, and the like. In several embodiments, some or all of the aforementioned sensor data 150 is generated by and received by the wearable electronic device from one of the one or more other electronic devices.

Sensor data 150 generated during the time interval is processed by sensor data analyzer 112 . In some embodiments, a subset of sensor data 150 is generated by performing statistical operations (eg, averaging, etc.) on sensor data 150 and processed by sensor data analyzer 112 . At task block 2, sensor data analyzer 112 analyzes sensor data 150 received from one or more sensors 110 and calculates analyzed sensor information 152 for each moment of interest, and the analyzed sensor information 152 is used to determine the user's Status (e.g., sedentary or non-sedentary, etc.). In some embodiments, calculated at regular time intervals (e.g., intervals in the range of 30 seconds - 1.5 minutes, 1 minute intervals, intervals in the range of 0.5 seconds - 1.5 seconds, 1 second intervals, etc.) Analyzed sensor information 152 for multiple moments of interest. In various embodiments, time intervals are configurable and dynamically adjusted (e.g., decreased or increased) based on various factors, etc., and/or can be automatically disabled by the user and/or manually disabled over time spans to save power.

In some embodiments, the analyzed sensor information 152 is a metabolic equivalent (MET) measure of the task, where each MET is determined for a moment of interest. The MET measure is a normalized measure of energy expenditure that increases with activity and is nonzero at moments of interest. For example, an inactive or sleeping state or a non-wearing state has a MET measurement close to 1.0, a user who is walking typically has a MET measurement greater than 2.0, and a user who is swimming has a MET measurement between 10.0 and 11.0. Although in some embodiments, the analyzed sensor information 152 is a MET measurement, various embodiments use different measurements (e.g., motion measurements indicating motion of the user wearing the wearable electronic device, heart rate measurements indicating the user's heart rate, etc.) . Motion measurements are sometimes referred to herein as movement measurements. Examples of motion measurements include the number of steps the user takes, the number of stairs the user climbs or descends, and so on. In various embodiments, a heart rate sensor (eg, heart rate monitor, etc.) generates heart sensor data indicative of the user's heart rate and calculates the user's heart rate measurement.

The user state tracking unit 190 uses the analyzed sensor information 152 at the moment of interest to generate the user's state 156 for different time periods. In some embodiments, each time period typically contains multiple contiguous moments of interest. In various embodiments, each time period is as small as a moment of interest. The user state tracking unit 190 includes a user state identifier 120 that classifies each moment of interest 154 into the user's state based on the analyzed sensor information 152 for that moment of interest. As indicated in task box 3A, the user's state is divided into a sedentary state and a non-sedentary state.

In some embodiments, the user state classifier 120 classifies the state of the user at the moment of interest 154 as sedentary (e.g., sitting, sitting and typing on a computer, typing on a computer, etc.) or non-sedentary ( For example, active, running, walking, exercising, dancing, swimming, etc.). It should be noted that, in various embodiments, the user consumes more energy when the user's state is classified as non-sedentary than when the user's state is classified as sedentary. Several methods for classifying the moment of interest 154 are described in more detail below with reference to FIGS. 6A-6B .

In various embodiments, MET measurements are used to determine a user's non-sedentary state and correlate the non-sedentary state with a particular type of activity of the user. For example, from the MET measurements, the user state classifier 120 determines whether the user is running, walking, sprinting, biking, swimming, or performing another type of non-sedentary activity.

As described in task block 3B, the time period detector 122 of the user state tracking unit 190 detects time periods during which the user's state is sedentary based on contiguous moments of interest during which the user's state is classified as sedentary. For example, according to some embodiments described below with reference to FIG. 2 , time period detector 122 determines that a time frame has a sedentary state when time period detector 122 determines that a time frame contains a contiguous moment of interest of a sedentary state.

The sedentary notification unit 192 uses the user's status 156 for different time periods to generate one or more sedentary alerts 158 to notify the user. Examples of one or more sedentary alerts 158 are provided below. One or more sedentary alerts 158 encourage the user to limit the length of the sedentary time period. At task box 4, the sedentary status monitor 124 of the sedentary notification unit 192 generates one or more sedentary alerts 158 (e.g., notifications, etc.) for providing to the user to encourage the user to change his/her sedentary behavior. Behavior. One or more sedentary alerts 158 are provided to the user via the user interface 126 (including a display device of the wearable electronic device). In some embodiments, the user receives one or more sedentary alerts via a vibration of the wearable electronic device, a message displayed on a display device of the wearable electronic device, and/or a sound emitted by a speaker within the wearable electronic device 158.

In some embodiments, the sensor data analyzer 112 is located within the wearable electronic device or within one of the other electronic devices. For example, a processor of one of the wearable electronic devices or other electronic devices performs the operations described herein as performed by the sensor data analyzer 112 . In several embodiments, the user state classifier 120 is located within the wearable electronic device or within one of the other electronic devices. For example, a processor of one of the wearable electronic devices or other electronic devices performs the operations described herein as performed by the user state classifier 120 . In various embodiments, the time period detector 122 is located within the wearable electronic device or within one of the other electronic devices. For example, a processor of one of the wearable electronic devices or other electronic devices performs the operations described herein as performed by the time period detector 122 . In several embodiments, the sedentary status monitor 124 is located within the wearable electronic device or within one of the other electronic devices. For example, a processor of one of the wearable electronic devices or other electronic devices performs the operations described herein as the sedentary status monitor 124 performs. In some embodiments, the user interface 126 is located within the wearable electronic device or within one of the other electronic devices. For example, a processor of one of the wearable electronic devices or other electronic devices performs the operations described herein as performed by the user interface 126 . Examples of processors include application specific integrated circuits (ASICs), programmable logic devices (PLDs), central processing units, microprocessors, controllers, microcontrollers, and the like.

In some embodiments, a different processor of the wearable electronic device performs operations instead of the processor of the wearable electronic device performing operations such as sensor data analyzer 112, user state classifier 120, time period detector 122, sedentary state monitoring The operations described herein are performed by the controller 124 and the user interface 126. For example, a processor of the wearable electronic device performs the operations described herein as performed by the sensor data analyzer 112, another processor of the wearable electronic device performs the operations described herein as performed by the user state classifier 120, the wearable electronic device Another processor of the wearable electronic device performs the operations described herein as performed by the time period detector 122, another processor of the wearable electronic device performs the operations described herein as performed by the sedentary status monitor 124, and another processor of the wearable electronic device A processor performs the operations described herein as performed by the user interface 126 . Similarly, in various embodiments, a different processor of one of the other electronic devices performs operations rather than a processor of one of the other electronic devices performing operations such as sensor data analyzer 112, user state classifier 120, time The operations described herein are performed by segment detector 122 , sedentary state monitor 124 , and user interface 126 .

In various embodiments, one or more processors of the wearable electronic device perform the tasks as performed by the sensor data analyzer 112, the user state classifier 120, the time period detector 122, the sedentary state monitor 124, and the user interface 126. operations described in this article. Similarly, in some embodiments, one or more processors of one of the other electronic devices perform the tasks as performed by the sensor data analyzer 112, user state classifier 120, time period detector 122, sedentary state monitor 124 and user interface 126 perform the operations described herein.

It should be noted that in embodiments where components 112, 120, 122, and 124 located in one of the other electronic devices perform the tasks described above with reference to task blocks 2 to 4, the wearable electronic device includes a display device to display one or Multiple sedentary alerts158. Additionally, in these embodiments, one of the wearable electronic device and the other electronic device communicate with each other via a communication medium (eg, Universal Serial Bus cable, wireless protocol over the air medium, serial cable, parallel cable, etc.) communication. An example of a wireless protocol includes Bluetooth™.

1B illustrates a flowchart of a method for tracking periods of sedentary time and having a user receive notifications based on periods of sedentary time, according to various embodiments described in this disclosure. At operation 102 of the method, time period detector 122 tracks time periods in which the user's status is determined to be sedentary (sometimes referred to herein as "sedentary time periods"). The time periods are determined based on MET measurements at each non-overlapping instant of interest within each time period. Additionally, in some embodiments, MET measurements are generated within the wearable electronic device based on sensor data 150 received from one or more sensors 110 ( FIG. 1A ) (eg, three-axis accelerometers and heart rate sensors, etc.). Examples of MET measurements include movement measurements calculated based on sensor data 150 from a motion sensor and heart rate measurements calculated based on sensor data 150 from a heart rate sensor (placed within the same wearable electronic device). If the length of the sedentary time period exceeds a predetermined threshold time period, the time period detector 122 determines that the user's status is sedentary.

At operation 104 of the method for tracking sedentary periods, an act of providing a notification to the user is performed based on the tracking at operation 102 to encourage the user to limit the length of the sedentary period. In some embodiments, operation 104 is performed within the wearable electronic device and the user responds by receiving a message on the display of the wearable electronic device, vibration of the wearable electronic device, and/or by a speaker within the wearable electronic device. sound to be notified.

In some embodiments, a notification as described herein is an electronic notification sent to a display device. For example, the processor renders the electronic notification for display on the display device. In various embodiments, electronic notifications are provided in the form of vibrations or sounds.

FIG. 2 illustrates the use of sedentary or non-sedentary user status to determine time periods in which the user status is sedentary, according to some embodiments described in this disclosure. As illustrated in FIG. 2 , states are graphed as sedentary or non-sedentary over time (eg, at each moment of interest, etc.). From sedentary or non-sedentary states, non-overlapping, continuous time periods are obtained. During each consecutive time period, the user is in a sedentary or non-sedentary state. Each continuous time period spans one or more moments of interest. Also, as illustrated in FIG. 2 , consecutive time periods have different user states, and are described by task box 3B.

In particular, FIG. 2 shows that successive time periods are obtained: a sedentary time period 252, a non-sedentary time period 254, and a sedentary time period 256, each spanning multiple contiguous moments of interest of the same user state. The sedentary time segment 252 contains 6 moments of interest. Each moment has a state classified as sedentary. In contrast, the non-sedentary period 254 contains 5 moments of interest, each with a state classified as non-sedentary. Transitions between the user's states are represented by edges of time periods (eg, when one time period ends and the next begins, etc.). For example, at the end of the sedentary period of time 252 and at the beginning of the non-sedentary period of time 254, the user's state transitions from a sedentary state to a non-sedentary state.

In some embodiments, time period detector 122 (FIG. 1A) detects and records alternating time periods in which the user's status is sedentary or non-sedentary. For example, the sedentary time periods 252 and 256 and the non-sedentary time period 254 illustrated in FIG. 2 are recorded over a time span (e.g., hours, days, weeks, etc.) to present to the user or to perform a sedentary session with the user. Behavior-related additional analysis. The determined period of time having a state (eg, sedentary state, non-sedentary state, etc.) It receives the determined time period and status as data from the wearable electronic device) and presents it to the user on the display device. In some embodiments, one of the other electronic devices generates the determined time period and state. The user then reviews his/her sedentary behavior indicated by the determined time period and status, and tracks his/her improvement over time.

In some embodiments, a user shares information about his/her sedentary behavior with friends, colleagues, or team members via a network (eg, a social network, etc.). Friends, colleagues or team members compete with each other based on their respective sedentary states determined from their respective recorded sedentary time periods.

In various embodiments, additional sensor data is used to additionally disambiguate between sedentary time periods and other time periods when the user is sleeping and/or not wearing the wearable electronic device. The wearable electronic device is capable of detecting periods of time during which the user is sleeping and/or not wearing the wearable electronic device. For example, one or more sensors 110 cannot detect information about the user (eg, motion sensor data, heart sensor data, steps taken, etc.). To further illustrate, when the user is not wearing the wearable electronic device, the one or more sensors detect that the number of steps taken by the user is zero for a period of time. The time period satisfies the motion-based criteria for MET measurements or sedentary time, but is not sufficient to be sedentary because the user is not wearing the wearable electronic device or the one or more sensors 110 detect that it is sleeping. Before or during making the determination of sedentary and non-sedentary states, the processor of the wearable electronic device or a processor of one of the other electronic devices filters out periods of time during which the wearable electronic device is not worn by the user (e.g., during sleeping, in the bath, etc., or during sleep) MET measurements.

Embodiments of the sedentary state monitor are described next.

Through automatic detection and tracking of time periods in which the user's status is sedentary, the user receives notifications encouraging him/her to change his/her behavior and be less sedentary. The notifications facilitate interrupting prolonged sedentary states and reduce the user's overall sedentary time. 3 is a block diagram of an embodiment of a sedentary state monitor 124 for informing a user based on tracking of sedentary time periods to encourage the user to change his/her sedentary behavior and Limit the length of your sedentary periods. The sedentary state monitor 124 includes a management unit 310 which receives the user's state over a period of time. For example, provide status at the end of a time period (after the user's previous status has changed). For example, following a transition between two consecutive states of the user, it is indicated to the management unit 310 that the current time period and state of the user has started. As another example, states are provided as a current stream containing information on transitions between two successive states. As yet another example, the status is provided in batches at regular intervals. For example, the current time period is thus X time long, and the management unit 310 detects a transition between two consecutive states during X time.

Although Fig. 3 shows the non-sedentary state transition manager 320 of the management unit 310, the sedentary alarm manager 330 of the management unit 310, and the non-sedentary goal manager 340 of the management unit 310, in some embodiments, the management unit 310 has more, fewer and/or different types of managers. In embodiments with multiple types of managers, one or some combination of these managers are used at different times to interact with the user, for example, based on the user's sedentary state through the user's wearable electronic device or through other One of the electronic devices to send notifications to the user to encourage the user to change or end sedentary behavior (as discussed in more detail below).

Embodiments describing behaviorally triggered alerts (eg, non-sedentary state transitions, etc.) follow.

According to several embodiments, for example, upon detecting that a sedentary period has ended and a non-sedentary period has begun (e.g., the user starts moving, etc.), and upon determining that the non-sedentary period exceeds a threshold period of time, the wearable electronic The user is notified on the device (via display of the notification, etc.). While in some embodiments the threshold time period is the same for all types of activity, in various embodiments the threshold time period for at least some types of activity is different. The user receives a notification (e.g., a message displayed on the display of the wearable electronic device, a vibration of the wearable electronic device, and/or a congratulatory sound on the wearable electronic device), etc., which informs the user that he/she has just finished sedentary period. The notifications are intended to encourage the user to stay mobile and remain active to limit the total amount of time in which the user is sedentary.

4 illustrates the delivery of a notification to a user based on the detection of the end of a sedentary period 424 and the beginning of a non-sedentary period 426 (a threshold period of time has been exceeded), according to some embodiments described in this disclosure. In some embodiments, the time period detector 122 ( FIG. 1A ) detects a user's sedentary time period 424 and upon detecting 412 that the user's status has changed from sedentary to non-sedentary for a threshold time period, the time period detector 122 Notification non-sedentary state transition manager 320 (FIG. 3), which delivers 414 notifications (eg, non-sedentary state transition notification information, etc.) to the user's wearable electronic device. As an example, when the set of one or more moments of interest contained in the non-sedentary time period 426 satisfies a threshold time period, it is detected that the user's status has transitioned from sedentary to non-sedentary. In some embodiments, when a user performs one of various activities (e.g., swimming, jogging, brisk walking, hanging out, etc.) is detected as non-sedentary.

In various embodiments, the time period detector 122 determines the non-sedentary time period 426 based on the state of the user at the moment of interest. In some embodiments, the end of the sedentary period 424 is detected when the user's non-sedentary state is detected.

In several embodiments, the non-sedentary state is detected based on the type of activity the user is performing. For example, when a user runs for 30 seconds (eg, the user's status is classified as "non-sedentary, running" for 30 seconds, etc.), a change in the user's status from sedentary to non-sedentary is detected and the user receives a notification. As another example, a change in the user's state from sedentary to non-sedentary is detected when the user sprints for at least 10 seconds (e.g., the user's state is classified as "non-sedentary, sprinting" for at least 10 seconds, etc.) And the user receives the notification. As yet another example, when the user walks briskly for 1 minute (e.g., the user's state is classified as "non-sedentary, walking" for 1 minute, etc.), a change in the user's state from sedentary to non-sedentary is detected and the user Receive notifications. As yet another example, when the user hangs out for at least 3 minutes (e.g., the user's status is classified as "non-sedentary, hanging out" for at least 3 minutes, etc.), a change in the user's status from sedentary to non-sedentary is detected and The user receives the notification.

In some embodiments, time period detector 122 detects sedentary time period 424 and detects 412 non-sedentary moments of interest (e.g., one or more moments of interest contained in non-sedentary time period 426, etc.) , the time period detector 122 notifies the non-sedentary state transition manager 320, the non-sedentary state transition manager 320 determines that the non-sedentary time period exceeds the threshold time period and delivers 414 a notification to the user's wearable electronic device about meeting the threshold time period . A notification that the threshold time period is met indicates that the user has completed the sedentary time period 424 and is now active. Notifications are designed to encourage users to be more active and interrupt sedentary periods more frequently.

In various embodiments, the notification that the threshold time period is met is in the form of a sentence or motivational statement or positive message displayed on a display device of the wearable device. Examples of a non-exhaustive list of exemplary motivational statements indicating whether a threshold time period is met include "well done", "excellent work", "keep moving", "keep going", "don't stop", "go more", "Sedentary for xx hours" (where xx is how long the user has been sedentary); "keep walking"; "walk for xx minutes" (where xx is a value between 1-20, such as 2); "you have been sitting for xx hours Stand up after yy minutes" (where xx is the number of hours the user has been sedentary and yy is the number of minutes); "You are on x steps. Can you do x+200?" (where x is the sedentary period since the end number of steps since); "Long walk?"; "Walking meeting?"; "Let's go!"; "Success!"; "Let's call you Butter because you're rolling!";" You're on fire!"; "Move as hard as you can!"; "Don't stop moving!!!"; "Grab a friend and keep walking!"; "You can't catch me!"; "Well done, you sat for about x hours and now you're finally on your feet";"Get up";"Walk like you just robbed the bank";"Stay on top!";"Go for it";"Get moving!";"Do it!"; "You did it!"; "Looks healthy!"; "Good for you"; "Great!"; "Score!"; and "Excellent!", etc.

An example of a rolling alert is described next.

In various embodiments, the user is notified by the wearable electronic device upon detection that the user has been sedentary for a threshold amount of time (sometimes referred to herein as a threshold sedentary period of time). When the user has been sedentary for an extended period of time, a sedentary alert is delivered to the user to inform him/her of the extended period of time and encourage him/her to end the extended period of time. The sedentary warning is a message displayed on a display device of the wearable electronic device, a sound emitted by the wearable electronic device, and/or a vibration of the wearable electronic device. The sedentary alert is intended to encourage the user to start moving and become active to end the sedentary period.

In some embodiments, the threshold sedentary period is 1 hour or 2 hours or 20 minutes or 40 minutes or seconds or minutes or hours. In various embodiments, the threshold sedentary time period is configurable, eg, the user selects the length of the sedentary time window after which he/she would like to be notified to end the sedentary time. In several embodiments, the threshold sedentary time period is dynamically adjusted (eg, decreased or increased) based on various factors, etc., and/or can be disabled automatically and/or manually by a user.

In some embodiments, the user sets preferences for the types of alerts to be received. For example the user selects a sound, a specific message to be displayed and/or a vibration. In various embodiments, the user sets the sedentary state monitor 124 (FIG. 1A) such that periods of sedentary time are monitored at specific time intervals. For example, a sedentary period or a non-sedentary period is monitored between 8 am and 8 pm of the day and not monitored for the rest of the day.

In some embodiments, via an input device of the wearable electronic device (eg, a keypad, a touch screen of a display device, a stylus, etc.), or via an input device of one of the other electronic devices (eg, a keypad, a display The device's touch screen, stylus, keyboard, mouse, etc.) receives input (eg, one or more preferences, etc.) from the user. In an embodiment where the input is received at the wearable electronic device and the sedentary status monitor 124 is internal to one of the other electronic devices, the input is communicated from the communication means of the wearable electronic device to the other electronic device a communication device. The communication device of one of the other electronic devices provides input to the sedentary status monitor 124 of one of the other electronic devices. Examples of communication devices include devices applying Bluetooth protocol, Internet Protocol (IP), Ethernet protocol, Transmission Control Protocol/IP (TCP/IP) protocol, Universal Serial Bus protocol, Serial Transmission Protocol, Parallel Transmission Protocol, and the like. In embodiments where the input is received at one of the other electronic devices and the sedentary status monitor 124 is located inside the wearable electronic device, the input is communicated to the wearable electronic device from a communication device in the other electronic device communication device. The communication device of the wearable electronic device provides input to the sedentary status monitor 124 of the wearable electronic device.

5 illustrates delivery of a sedentary alert to a user based on detection of a sedentary period exceeding a sedentary period threshold, according to various embodiments described in this disclosure. The period detector 122 ( FIG. 1A ) detects 516 a sedentary period 528 of the user and upon detecting 516 that the sedentary period 528 exceeds a threshold sedentary period ΔT, the sedentary alert manager 330 delivers 518 a sedentary alert (e.g., Sedentary warning notification information, etc.) to the user's wearable electronic device. The sedentary alert indicates that the user has spent more than ΔT in a sedentary state and encourages him/her to end the sedentary state by performing more active tasks (e.g., walking, running) or performing physical activities that are more energy-intensive than sedentary, etc. Sit time period 528. The sedentary alert is intended to inform the user of his/her sedentary behavior and encourage him/her to be more active. In some embodiments, the sedentary warning is in the form of a vibration of the wearable electronic device or a sound emitted by the wearable electronic device through a speaker of the wearable electronic device or a message displayed on a display device of the wearable electronic device.

A non-exhaustive list of sedentary alerts includes: "Time to move!"; "How about a walk?"; Get up"; "Please stand up"; "How about a walk?"; "Get up!"; "Take a break"; "Stretch your legs"; "You have been stationary for xx minutes" (where xx is the user has been sedentary time); "Where you are today is what your mind decides!"; "Take care of your body"; "Get up!"; "Don't just sit there"; "Be a role model"; "Get up, stand up" ;"Get up, workout!";"You've been sitting for an hour";"Do you know what time it is!";"Oh! Let's get down!";"Give me the steps";"It's never too late to move !";"It's time to move"";"Let's move";"Move! move! move! "; "I'm bored. Let's shake it up! "; "Go get them!" "; "You can do anything you let your mind do"; "Now it's time to fly!" "; "I dare say you're going to move!" "; "A few more steps please"; "Move your ass"; "Get up and get up"; "Stretch"; "Walk"; "Get up and start walking"; "Walk"; "Grab a friend and walk" Go"; "When you are currently sedentary, stay sedentary! "; "I believe you can fly!" "; "What did you do today to make me proud?"; "I want to run"; "Catch it today!" "; "Run away!" ";" I'm behind you! ";"The British are coming";"Tick tock, your blood sugar is rising";"Shut up and walk";"Desire for steps";"Error: Step count too low";"Wrong step count ";"Have you forgotten how it feels to walk?";"If you were my Fitbit, I would walk you";"It's been a while";"It's time to get up! "; "Move"; "Stop being Eeyore"; "Jump up"; "Jump up like a rabbit"; "It's that time again! "; "Let's go get some water"; "Let's go for a tour"; "Would you like to take a walk?"; "Stretch your feet! "Stretch"; "Walk two streets"; "Go out now"; "Go out"; "Left left! ’; and ‘Let’s find some stairs! ”, etc. In some embodiments, the user alternatively or additionally receives one or more icons and/or one or more animated images (e.g., animated feet, animated steps, etc.) displayed on the display device of the wearable electronic device ) and one or more icons and/or one or more animated images indicating that the user has been sedentary for more than a threshold sedentary time period ΔT.

In various embodiments, upon receiving the sedentary alert, the user ends the sedentary period. In several embodiments, upon receiving a sedentary alert, the user remains sedentary and continues to receive sedentary alerts from the sedentary state monitor 124 at regular intervals to encourage him/her to move. For example, users receive sedentary alerts every hour.

In some embodiments, if the user ends the sedentary time period 528, the user receives a congratulatory message (also sometimes referred to herein as a celebration message) from the sedentary status monitor 124 via the wearable electronic device, encouraging him/her to end his/her efforts. Sedentary time period 528 (described above). The celebration message is one of the celebration messages described otherwise below.

An example of a mini target alert is described next.

In some embodiments, the user is encouraged to achieve a mini-goal during a predetermined time window (eg, 250 steps, or 15 minutes of consecutive non-sedentary moments of interest, etc.). A mini-goal is a step toward achieving a predetermined goal. The non-sedentary goal manager 340 (FIG. 3) tracks the user's sedentary time periods and non-sedentary activities, and the non-sedentary goal manager 340 interacts with the display device of the wearable device and sends one or more notifications (e.g., mini Goal notifications, etc.) to the wearable device's display to provide information about the user's progress towards the mini-goal. For example, the non-sedentary goal manager 340 sends a progress indication to be notified via a vibration of the wearable electronic device and/or a message displayed on a display of the wearable electronic device and/or a sound emitted by a speaker of the wearable electronic device The remaining activities to be performed by the user to achieve the mini-goal before the end of the predetermined time window. A non-exhaustive exemplary list of notifications and messages a user receives as part of a mini-goal includes "xx steps left"; "xx steps!"; "xx steps left"; "take xx steps before 3pm";" xx steps to hourly goal"; "10 minutes to take xx steps!"; "walked xx/yy steps, xx steps to be taken!"; "each step count! xx steps to be taken in this hour!"; just walk xx steps until yy"; and "walk xx (animated feet/steps)", where xx is replaced by the number of steps remaining and yy is replaced by the total number of steps set to achieve the mini-goal.

In some embodiments, the user receives notifications (e.g., messages, mini target notification message, etc.), asking him/her to start becoming active, e.g., walking, running, etc., and then via vibration of the wearable electronic device and/or a message on the display device of the wearable electronic device and/or by Instead of receiving an indication of how many steps remain or the length of activity remaining to achieve the mini-goal, wear the sound from the speaker within the electronic device to receive a "celebration message" for achieving the mini-goal. For example, the non-sedentary goal manager 340 determines that the mini-goal has been achieved and via vibration of the wearable electronic device and/or a message on a display of the wearable electronic device and/or a sound emitted by a speaker within the wearable electronic device Instead, provide a notification to the user that the mini-goal has been achieved. Mini-goals are sometimes referred to herein as mini-celebrations. For example, a mini-celebration is "buzz + smile" when the user reaches yy steps during the predetermined time window set for the mini-goal (eg, during 1 hour, etc.). Buzzing is an example of vibration of a wearable electronic device. In some embodiments, the wearable electronic device includes a haptic feedback device that vibrates to provide haptic feedback to the user to provide notifications to the user.

While in some embodiments, the predetermined time window for achieving the mini-goal (e.g., non-sedentary goal, etc.) is 1 hour, in various embodiments, a different predetermined time window is used (e.g., approximately 10 minutes to 6 hours, or approximately 20 minutes to 3 hours, every 2 hours, etc.). In several embodiments, the predetermined time window for achieving the mini-goal (eg, non-sedentary goal, etc.) is configurable, eg, the user selects the length of the predetermined time window for achieving the mini-goal by setting a preference in the manner described above. In some embodiments, the predetermined time window for achieving the mini-goal is dynamically adjusted (eg, decreased or increased) by the sedentary status monitor 124 based on various factors, among others. In some embodiments, the predetermined time window for achieving the mini-goal can be automatically disabled by the sedentary status monitor 124 and/or manually disabled by the user.

In various embodiments, the user additionally determines a preference regarding the timing of receiving notifications and reminders of his/her progress towards the mini-goal and either via an input device of the wearable electronic device or via an input device of one of the other electronic devices Preferences are provided to the sedentary state monitor 124 . For example, a user wants to receive a notification a few minutes before the end of a predetermined time window (eg, 50 minutes in an hour, etc.), before a mini-goal is achieved. Notifications contain information indicating the mini-goal to be achieved and the remaining activity to be performed to achieve the mini-goal (eg, non-sedentary minutes, steps, etc.). If the user completes the mini-goal before the end of the predetermined time window, the user receives a reward message from the sedentary state monitor 124 and a reward for that achievement from the sedentary state monitor 124 .

Herein, a non-exhaustive exemplary list of celebratory messages received by a user for achieving a mini-goal is presented as: "";"Well done!";":-D :-D :-D";":): ) :) :)"; "xx/yy!!!"; "Another hour!"; ;"Excellent!";"Every extra step counts!";"You = stepper";"You = on fire!";"You = Awesome!";"Steps per hour champion" ; "xx steps aren't even that many"; and "my hero", where xx is replaced by the number of steps completed during the predetermined time window allotted to the mini-goal, and yy is replaced by the number of steps set to achieve the mini-goal. Also in some embodiments, users compete with friends via social networking to achieve the most mini-goals.

In various embodiments, the non-sedentary goal manager 340 tracks and records mini-goals set and achieved by the user and presents the set and/or achieved mini-goals to the user. The mini-goal is presented to the user on a display device of the wearable electronic device or one of the other electronic devices, and the display device receives information from the wearable electronic device via a communication device of the wearable electronic device and a communication device of one of the other electronic devices. Receive mini goals. The user then views his/her sedentary behavior and tracks his/her improvement in achieving the mini-goal over time.

In several embodiments, the sedentary alarm manager 330 is distinguished from the non-sedentary target manager 340 in that the sedentary alarm manager 330 works on the current sedentary time period that starts when the user's state transitions to sedentary, while The non-sedentary goal manager 340 closes the set time windows regardless of whether the state was sedentary at the beginning of each time window. As previously discussed, two or more managers are used in combination to interact with the user via the sedentary state monitor 124 and the wearable electronic device to change his/her sedentary behavior.

An example of a learning alert is described next.

In some embodiments, sedentary learning unit 350 ( FIG. 3 ) of sedentary state monitor 124 is coupled to managers 320 , 330 , and 340 and receives information from each of user managers 320 , 330 , and 340 via sedentary learning. Status monitor 124 ( FIG. 1A ) sends notification information (eg, one or more notifications, etc.) to the user and determines which of the one or more notifications affect modifying the user's sedentary behavior. For example, the sedentary learning unit 350 determines which of the one or more notifications successfully changed the user's general sedentary behavior by limiting the length of the sedentary time period.

Although in some embodiments, each of managers 320, 330, and 340 transmits information about sending one or more notifications (for example, non-sedentary state transition notification information, sedentary warning notification information, and mini-goal notification information, etc.) In various embodiments, managers 320, 330, and 340 transmit more, less, or different data as part of the notification message. For example, managers 320 , 330 , and 340 transmit a type of notification (eg, message, vibration, and/or sound) to sedentary learning unit 350 . As another example, the managers 320 , 330 , and 340 transmit information on the results of notifications sent to the user (eg, whether the user has finished his/her sedentary period, etc.) to the sedentary learning unit 350 .

In some embodiments, sedentary learning unit 350 receives sensor data 150 ( FIG. 1A ) from one or more sensors 110 ( FIG. 1A ) to determine whether the communicated notifications affect modifying the user's sedentary behavior. The sedentary learning unit 350 records the sensor data 150 over time to learn which types of notifications (eg, message personality or tone, etc.) and which content (eg, time, location, etc.) have a desired impact on the user. Examples of desired effects include a reduction in long sedentary periods, a reduction in the number of sedentary states over time, and the like. The sedentary learning unit 350 determines improved preferences and settings (eg, configuration parameters, etc.) for configuring at least one of the managers 320, 330, and 340 based on the received notification information.

The sedentary learning unit 350 learns the user's behavior in response to notifications from the managers 320, 330, and 340 and reacts to the user's behavior to improve the user's response to the notifications. For example, the sedentary learning unit 350 changes the configuration of one of the managers 320, 330, and 340 (e.g., by transmitting configuration parameters to that manager, etc.) The sitting learning unit 350 determines when the user does not respond to notifications at another specific time of day. As another example, the sedentary learning unit 350 changes the configuration of the sedentary alarm manager 330 by modifying the length of the threshold sedentary time period after sending the notification to the user. As yet another example, the sedentary learning unit 350 modifies the type of notifications sent to the user, e.g., one of the configuration managers 320, 330, or 340 to send alerts that would replace vibration (e.g., buzzing, etc.) on the wearable electronic device. Displaying a message on the display device or generating a sound emitted by the speaker of the wearable electronic device instead of vibration or changing the sound emitted or changing the tone of the message or modifying the type of notification.

In some embodiments, sedentary learning unit 350 changes a number of configuration parameters such that one of managers 320, 330, and 340 operates at a given time of day. For example, the sedentary learning unit 350 determines that between certain hours of the day (e.g., 8 am to 12 noon, etc.), the user is more responsive to notifications received from the non-sedentary state transition manager 320 than the user is to notifications received from the sedentary state transition manager 320. The response to the notification received by the alert manager 330 is good. In this example, the sedentary learning unit 350 determines configuration parameters that prohibit the use of the sedentary alert manager 330 during those hours (eg, 8 am to 12 noon, etc.). Although a sedentary alert manager 330 and a non-sedentary state transition manager 320 are described in this example, in various embodiments, the sedentary learning unit 350 determines configuration parameters to disable or enable another manager (e.g., non-sedentary state transition manager 320). target manager 340, etc.), and/or determine other hours of the day during configuration managers 320, 330, 340. While in several embodiments the sedentary learning unit 350 changes the configuration of at least one of the managers 320, 330, 340, in some embodiments the sedentary learning unit 350 transfers at least one of the managers 320, 330, 340 The configuration of one is suggested to the display device of the wearable electronic device for approval by the user prior to the configuration of at least one of the managers 320, 330, 340 with changed configuration parameters.

In various embodiments, the sedentary learning unit 350 allows the user to silence the sedentary warning notification, so that the wearable electronic device reminds the user to perform non-sedentary activities later. The sedentary learning unit 350 records data related to an action performed by the user via the input device of the wearable electronic device or one of the other electronic devices, related to the action of stopping the sedentary warning notification information. For example, the data related to the action of snoozing includes the type of notification of snoozing, the time of notification of snoozing, the state of the user at that time (for example, sedentary, non-sedentary, etc.), the geographical location of the notification of snoozing, etc. Sedentary learning unit 350 uses the motion-related data to change the configuration of one or more of managers 320 , 330 , and 340 . For example, if a user silences one or more of the alarm managers 320, 330, and 340 at a particular time of day, then the configuration is changed in the managers to avoid operation during that time. This serves to improve the user's experience and instill greater confidence in the wearable electronic device. In some embodiments, the sedentary learning unit 350 is implemented by implementing one or more of the following: decision trees, random forests, support vector machines, neural networks, K-nearest neighbors, Naive Bayes, and hidden Markov models.

In various embodiments, a user is able to set preferences for the types of notifications received based on his/her sedentary behavior. For example, a user can select a subset of the subunits of the sedentary state monitor 124 (e.g., non-sedentary state transition manager 320, sedentary state transition manager 320, sedentary state Alert Manager 330, Non-Sedentary Object Manager 340, etc.) are used to notify the user based on his/her sedentary behavior. In addition, the user is able to select a sound, a specific message to be displayed on the display device of the wearable electronic device, and vibration for each type of message received on the wearable electronic device. A combination of the types of notifications the user chooses to receive at the same time. The user configures the sedentary state monitor 124 via the input device of the wearable electronic device or the input device of one of the other electronic devices so that the sedentary time period is monitored within a specific time interval. For example, a user wants to monitor sedentary or non-sedentary periods of the day between 8:00 am and 8:00 pm.

An embodiment describing the classification of a user's status based on MET measurements follows.

In some embodiments, MET measurements are used to determine the sedentary state or the state of a non-sedentary user. For this reason, the MET measure is sometimes referred to as the sedentary index. User state classifier 120 (FIG. 1A) receives the MET measurements for the time of interest and determines whether the MET measurements are less than a predetermined threshold. When the MET measurement is less than a predetermined threshold, the user state classifier 120 classifies the user state at that moment of interest as a sedentary state. When the MET measurement is greater than a predetermined threshold, the user state classifier 120 classifies the user state at that moment of interest as non-sedentary and the user state classifier 120 determines the user as active.

FIG. 6A illustrates a record of MET measurements at consecutive moments of interest over a period of time and a user status at each moment of interest based on the MET measurements, according to some embodiments described in this disclosure. Classification of user status 612 by classifier 120 (FIG. 1A). According to several embodiments described in this disclosure, the user state classifier 120 classifies 614 the user state 612 at a moment of interest as non-sedentary based on the MET measurement at that moment of interest exceeding a threshold MET value. Sensor data analyzer 112 (FIG. 1A) generates MET measurements for multiple instants of interest (e.g., F ₁ , F ₂ . . . F _N , etc.), and user state classifier 120 compares each MET measurement (e.g., ) and the threshold MET value are used for the determination 630 of the sedentary state. The MET measurements 624 are all less than the threshold MET value (determined by the user state classifier 120), and the user state classifier 120 classifies 614 each time of interest of the MET measurements 624 and stores them in a memory device (e.g., a wearable electronic device or one of the other electronic devices, computer readable media, etc.) records the sedentary state. In contrast, the MET measurements 62 all exceed the threshold MET value (determined by the user state classifier 120), and the user state classifier 120 records each moment of interest of the MET measurements 626 within the memory device as a non-sedentary state . The user state classifier 120 correlates 628 the MET measurements with sedentary and non-sedentary moments of interest. For example, both MET measurements 628 are greater than a threshold MET value (determined by user state classifier 120 ), and user state classifier 120 identifies each moment of interest 628B of both MET measurements 628 as a non-sedentary state. The two other illustrated MET values of the MET measurement 628 are less than the threshold MET value (determined by the user state classifier 120 ). User state classifier 120 identifies each moment of interest 628A and 628C for the two other MET values as a sedentary state. In some embodiments, the threshold MET value is in the range of 0.8-1.8 MET (eg, 1.5 MET, etc.). The classification 614 of moments of interest illustrated at FIG. 6A yields the sedentary and non-sedentary moments of interest illustrated in FIG. 2 .

FIG. 6B illustrates a record of MET measurements at consecutive moments of interest over a period of time and a user status at each moment of interest based on the MET measurements, according to embodiments described in this disclosure. Classification of user states by classifier 120 (FIG. 1A). The state of the user at a moment of interest is classified as non-sedentary based on the MET measurement at that moment of interest exceeding a first threshold MET value. The state of the user at a time of interest is classified as sedentary based on the MET measurement at that time of interest being less than a second threshold MET value. Furthermore, the state of the user at the moment of interest is classified as sedentary if the MET value exceeds the second threshold MET value, is less than the first threshold MET value, and is additionally preceded and followed by a moment of interest with a sedentary state. In some embodiments, if the MET measurement associated with each moment is between a first and second threshold MET value, and a set of N consecutive moments of interest are immediately preceding and following the moment of interest with a sedentary state , then classify a set of N consecutive moments as sedentary. Examples of N consecutive moments of interest include moments of interest, each occurring at 1 minute intervals (for example, where N is between 1 and 5) or each occurring at intervals between 1 minute and 5 minutes Or each with 1 second intervals (for example, where N is between 1 and 300) or occurs at intervals between 1 second and 300 seconds, etc. If N moments of interest occur at longer intervals (e.g., every 10 minutes, etc.), then N is smaller (e.g., 2, etc.). In the embodiments discussed above, the second threshold MET value is less than the first threshold MET value.

User state classifier 120 compares each MET measurement generated by sensor data analyzer 112 with a first threshold MET value (for recording non-sedentary state 632 ) and with a second threshold MET value (for recording sedentary state 634 ). Compare. The user state classifier 120 performs recording of the non-sedentary state 632 and the sedentary state 634 in the memory device. MET measurements 646 are all greater than the first threshold MET value, and user state classifier 120 records each time of interest for MET measurements 646 determined by user state classifier 120 to have a non-sedentary state. The MET measurements 644 are all less than the second threshold MET value (determined by the user state classifier 120 ), and the user state classifier 120 records each moment of interest with a MET measurement 644 of the sedentary state. In contrast, some MET measurements 648 exceed the second threshold MET value but are less than the first threshold MET value (determined by the user state classifier 120), while other MET measurements 648 are less than the second threshold MET value (determined by the user state classifier 120 definite). A first moment of interest of a set of contiguous moments of interest for MET measurements 648 has a MET value (determined by user state classifier 120) that is less than a second MET threshold, while a second moment of interest for MET measurements 648 and A third moment of interest has a MET value between the first and second threshold MET values (determined by the user state classifier 120 ), immediately followed by a moment of interest with a MET value less than the second threshold MET value. In this example, user state classifier 120 determines all moments of interest of MET measurements 648 as having a sedentary state even though two moments within that set of contiguous moments have MET measurements that exceed the second threshold MET value.

As described above, in some embodiments, MET measurements determine the state of a non-sedentary user associated with a particular type of activity by the user. For example, from the MET measurements, the user state classifier 120 determines whether the user is running, walking, sprinting, biking, swimming, or performing another type of non-sedentary activity. To further illustrate, if the MET measurement is in the range of 2.5 to 3.2, the user's status is classified as "non-sedentary, cycling". As another example, if the MET measurement is in the range of 3.2 to 3.8, the user's status is classified as "non-sedentary, walking". As yet another example, if the MET measurement is between 6.7 and 7.3 (eg, 7.0, etc.), the user's status is classified as "non-sedentary, jogging."

An embodiment describing classification based on other sensor information follows.

In some embodiments, user state classifier 120 is based on sensor data 150 ( FIG. 1A ) received from one or more sensors 110 ( FIG. 1A ) (eg, motion sensor data and/or biometric data, etc.) without MET measurements generation to determine the sedentary state of the user at the moment of interest. For example, a user's sedentary state is determined based on motion measurements (eg, also sometimes referred to as ambulatory measurements, etc.) and/or heart rate measurements without calculation of MET measurements.

A description follows of an exemplary apparatus having automatic detection of a user's sedentary or non-sedentary state and providing notifications to the user based on the sedentary state.

As previously described, although in some embodiments, one or more of the above operations are implemented in a wearable electronic device, in various embodiments, one or more operations are distributed between electronic devices, for example, Wearable electronic devices and other electronic devices, etc. Figure 7 illustrates an example of one such distribution. FIG. 7 is a block diagram illustrating a wearable electronic device 702 and an electronic device 700 implementing operations disclosed in accordance with various embodiments described in this disclosure. The electronic device 700 is an example of one of other electronic devices. Wearable electronic device 702 includes processor 742 and one or more sensors 110 . In some embodiments2, multiple processors are used in wearable electronic device 702 instead of processor 742 .

In some embodiments, the one or more sensors 110 include motion sensors 727, examples of which include multi-axis accelerometers, gyroscopes, gravity sensors, rotational vector sensors, and magnetometers. Additionally, in various embodiments, one or more sensors 110 include one or more other sensors 714 (including photoplethysmography sensor 720 ). In several embodiments, the one or more other sensors 714 include a temperature sensor 721 , an ambient light sensor 722 , a galvanic skin response sensor 723 , a capacitive sensor 724 , a humidity sensor 725 , and an acoustic sensor 726 .

The wearable electronic device 702 also includes a non-transitory machine-readable storage medium 718 that includes the sensor data analyzer 112 as discussed herein above. When executed by processor 742 , sensor data analyzer 112 causes wearable electronic device 702 to generate analyzed sensor information 152 for a moment of interest. Wearable electronic device 702 performs functionality related to user state classifier 120, time period detector 122, and/or sedentary state monitor 124, some or all of which are included in sedentary tracking and notification module (STNM) 750 , STNM 750 is stored in non-transitory machine-readable storage medium 718. When executed by the processor 742, the STNM 750 causes the wearable electronic device 702 to perform the corresponding operations discussed herein above. The wearable electronic device 702 also includes the user interface 126 having a display device 732 . Examples of display devices include liquid crystal display (LCD) display devices, light emitting diode (LED) display devices, plasma display devices, and the like. In some embodiments, user interface 126 includes a speaker, tactile screen, and/or vibration mechanism (e.g., tactile communication device, joystick, somatosensory communication device, etc.) to allow communication and interaction with a user wearing wearable electronic device 702 .

In some embodiments, one or more other sensors 714 are not placed inside wearable electronic device 702 . One or more other sensors 714 are distributed around the user. For example, one or more other sensors 714 are placed on the user's chest or on a mattress on which the user is lying or on a bedside table where the user is sitting while the user is wearing the wearable electronic device 702 .

FIG. 7 also includes an embodiment of an electronic device 700, such as a server, tablet, smartphone, etc. (including applications) including hardware and software. In some embodiments, the electronic device 700 performs functionality related to the user state classifier 120, the time period detector 122 and/or the sedentary state monitor 124, some or all of which are contained in the STNM 750, the STNM 750 stored in the non-transitory machine-readable storage medium 748 of the electronic device 700. For example, the STNM 750 is stored in the non-transitory machine-readable storage medium 748 of the electronic device 700 for execution by the processor 752 of the electronic device 700 rather than in the non-transitory machine-readable storage of the wearable electronic device 702 Medium 718. In some embodiments, sensor data analyzer 112 is stored in non-transitory machine-readable storage medium 748 (instead of non-transitory machine-readable storage medium 718 ), and is executed by processor 752 .

When executed by the processor 752, the STNM 750 causes the electronic device 700 to perform the corresponding operations discussed herein above. In some embodiments, electronic device 700 includes virtual machines (VMs) 762A through 762R, each of which executes software instance 766 or software instance 768 of STNM 950 . Hypervisor 754 presents a virtual operating platform for virtual machines 762A-762R.

Wearable electronic device 702 collects one or more types of sensor data 150 (eg, biometric data, etc.) from one or more sensors 110 and/or external devices, and then utilizes sensor data 150 in various ways. Examples of biometric data include data pertaining to physical characteristics of a human body (eg, heartbeat, heart rate, sweat level, etc.). Other examples of sensor data 150 include data related to physical interaction of the human body and the environment (eg, accelerometer readings, gyroscope readings, etc.). Examples of external devices include external heart rate sensors or monitors, eg, chest strap heart rate sensors or monitors, and the like. Examples of utilizing sensor data 150 in various ways include making calculations based on sensor data 150, storing sensor data 150, storing calculations in non-transitory machine-readable storage medium 718, automatically acting on sensor data 150, automatically acting on calculations , transfer the sensor data 150 to the communication device (e.g., one or more network interface controllers 744, etc.) to the communication device. Examples of automatically acting on calculations include automatic watch checking and hand waving gesture detection. Wearable electronic device 702 also receives data (eg, notifications, etc.) from one of the other electronic devices for storage and/or display on display device 732 as described herein.

In some embodiments, electronic device 700 includes a display device for presenting any of the notifications described herein, for example, non-sedentary state transition notification information, sedentary warning notification information, and mini goal notifications received from wearable electronic device 702 information etc. For example, the sedentary state monitor 124 of the wearable electronic device 702 generates a notification and sends the notification to the display device of the electronic device 700 via the communication device of the wearable electronic device 702 and the communication device of the electronic device 700 for viewing on the display device 700 display.

In various embodiments, the sensor data 150 is acquired by the wearable electronic device 702 and sent via the communication device of the wearable electronic device 702 and the communication device of the electronic device 700 to the STNM 750 of the electronic device 700 for performing the operations described herein.

In several embodiments, the notification is generated by the electronic device 700 and sent from the one or more network interface controllers 744 to the communication device of the wearable electronic device 702 via a computer network for display of the notification on the display device 732 . In various embodiments, the sedentary status monitor 124 of the electronic device 700 generates a notification and sends the notification to the display device 732 via the communication device of the electronic device 700 and the communication device of the wearable electronic device 702 for communication on the display device 732 show.

8 is a block diagram of an embodiment of a wrist-worn electronic device with buttons, a display, and a wrist strap for securing the wrist-worn electronic device to a user's forearm, according to several embodiments described in this disclosure. For example, FIG. 8 depicts a wearable electronic device 702, such as that illustrated in FIG. 7 , and is worn on a user's forearm, like a wrist watch. In FIG. 8 , the wrist-worn electronic device has a housing 802 containing electronics (e.g., the components illustrated in FIG. 7 , etc., associated with the wrist-worn electronic device), buttons 804, and Visual display screen 806 . Display screen 806 is display device 732 (FIG. 7). Wristband 808 is integrated with housing 802 .

In some embodiments, the wrist-worn electronic device incorporates one or more user interfaces including, but not limited to, visual, auditory, touch/vibration, or combinations thereof. In some embodiments, the wrist-worn electronic device provides tactile feedback (eg, via vibration of a motor). In some implementations, one or more sensors 110 (FIG. 1A) are used as part of one or more user interfaces, for example, an accelerometer sensor is used to detect when the user taps the housing of the wrist-worn electronic device with a finger or other object. 802 and then interpreting such data as user input for controlling the wrist-worn electronic device. For example, the wrist-worn electronic device recognizes a double tap of the housing 802 of the wrist-worn electronic device as user input.

Although FIG. 8 illustrates an implementation of a wrist-worn electronic device, in some embodiments, the wrist-worn electronic device has other shapes and sizes for coupling to (e.g., securing, wearing, carrying, etc.) the user's body or clothing. . For example, wrist-worn electronic devices are designed to be inserted into and removed from multiple compatible housings or housings or supports, such as a wristband worn on the user's forearm or a back pod attached to the user's clothing. As used herein, the term "wristband" refers to a strap designed to fully or partially encircle a user's forearm near the wrist joint. The straps are continuous (eg, without any breaks) or discontinuous or simply open. Examples of continuous straps include straps that stretch to conform to the user's hand or have stretches similar to a watch strap. Examples of discontinuous straps include clasps or other connected straps (similar to wrist straps) that allow the strap to close. An example of an open strap is one with a C shape that clasps the user's wrist.

It should be noted that in some embodiments, the user accesses information (eg, notifications, etc.) after logging into the user account. For example, a user provides his/her user information (eg, username, password, etc.), and when the server authenticates the user information, the user logs into the user account. In these embodiments, notifications are posted within the user's account. User accounts are stored on the server.

In some embodiments, the user accesses the user account to view the graphs illustrated in Figures 2, 4, 5, 6A, and 6B. The graph is viewed on a display device of the wearable electronic device or a display device of one of the other electronic devices.

It should be noted that in an embodiment, one or more features from any embodiment described herein are combined with one or more features of any other embodiment described herein without departing from each embodiment described in this disclosure range.

While the invention has been described in terms of several embodiments, those skilled in the art will recognize that the invention is not limited to the described embodiments, but can be practiced with modification and alteration within the spirit and scope of the appended claims. Accordingly, the descriptions are to be regarded as illustrative rather than restrictive.

Claims (30)

1. a method, including:

The sampling of the sensor information in the moment multiple interested obtained from wearable electronics by server reception, described sensor information generates when the user associated with described wearable electronics performs one or more activity, and described sampling is that the metabolic equivalent of normalized task is measured；

Sampling based on relatively each described moment interested and predetermined threshold, be categorized as at least one in sitting state and non-sitting state by the processor of described server by the sampling in each described moment interested；

Detected the multiple continuous samplings in described sampling by described processor and indicate the time period of described sitting state；

Determined that whether the described time period is more than threshold time period by described processor；And

When determining that the described time period is more than described threshold time period, user described in described processor identification it is in sitting state.

2. the method for claim 1, also includes:

Electronic notification is generated in response to identifying described user to be in described sitting state；And

Via computer network, described electronic notification being sent to described wearable electronics from described server and be used for presenting described electronic notification on described wearable electronics, wherein said electronic notification comprises user described in suggestion and terminates the message of described sitting state.

3. the method for claim 1, also includes:

Determine that the value of a sampling in the sampling in one of described moment interested is less than described predetermined threshold；And

It is categorized as that there is described sitting state in response to determining that the one in described sampling is sampled by described value less than described predetermined threshold.

4. the method for claim 1, each being classified as in wherein said sampling has described sitting state or described non-sitting state or sleep state.

5. the method for claim 1, each being classified as in wherein said sampling has described sitting state or described non-sitting state or the state indicating described user not dress described wearable electronics.

6. the method for claim 1, also includes:

Detected the multiple continuous samplings in described sampling by described processor and indicate the time period of described non-sitting state；

Determined that the plurality of continuous sampling in described sampling indicates the described time period of described non-sitting state more than predetermined amount of time by described processor；And

When the plurality of continuous sampling in determining described sampling indicates the described time period of described non-sitting state more than described predetermined amount of time, user described in described processor identification it is in described non-sitting state.

7. method as claimed in claim 6, also includes:

Determined that the multiple continuous samplings in described sampling indicate the time period of described non-sitting state adjacent with the time period of the multiple continuous samplings described sitting state of instruction in described sampling by described processor；

When multiple continuous samplings in determining described sampling indicate the time period of described non-sitting state adjacent with the time period of the multiple continuous samplings described sitting state of instruction in described sampling, user described in described processor identification it is transformed into described non-sitting state from described sitting state；And

When identifying that described user is converted to described non-sitting state, via computer network, electronic notification being sent to described wearable electronics from described server and be used for the display via described wearable electronics, described electronic notification comprises stimulus messages.

8. the method for claim 1, the metabolic equivalent measurement of wherein said task is the measurement of energy expenditure, each non-zero in the metabolic equivalent measurement of wherein said task.

9. the method for claim 1, also includes:

Determine that the value of a sampling in the sampling in one of described moment interested is more than described predetermined threshold；And

It is categorized as that there is described non-sitting state in response to determining that the one in described sampling is sampled by described value more than described predetermined threshold.

10. the method for claim 1, also includes:

Determine that the value of a sampling in the sampling in one of described moment interested is more than the second predetermined threshold higher than described predetermined threshold；And

It is categorized as that there is described non-sitting state in response to determining that the one in described sampling is sampled by described value more than described second predetermined threshold.

11. the method for claim 1, also include:

Determining that the value of the particular sample in the sampling in moment interested more than described predetermined threshold and is less than the second predetermined threshold, described second predetermined threshold is higher than described predetermined threshold；

Determine and correspond to described sitting state prior to the value of a sampling in the sampling in the moment interested in described moment interested；

After determining, one in the sampling in the rear moment interested value sampled in the described moment interested corresponds to described sitting state；And

Described particular sample in described sampling is categorized as having described sitting state.

12. the method for claim 1, also include:

Determine that the value of sampling in one group of continuous print moment interested is each more than described predetermined threshold and less than the second predetermined threshold, described second predetermined threshold is higher than described predetermined threshold；

Determine the moment interested prior to described one group of continuous print moment interested sampling in the value of corresponding to described sitting state；

In the value of in the sampling in rear moment interested of described one group of continuous print moment interested corresponding to described sitting state after determining；And

By the described sampling in moment interested for described one group of continuous print be each categorized as there is described sitting state.

13. methods as claimed in claim 12, also include:

By the described sampling in moment interested for described one group of continuous print be each categorized as there is described sitting state before, determine that the size in described one group of continuous print moment interested is less than size threshold value.

14. the method for claim 1, the wherein said moment interested occurs with regular time interval.

15. 1 kinds of methods, including:

The sampling of the sensor information in the moment multiple interested obtained from wearable electronics by server reception, described sensor information generates when the user associated with described wearable electronics performs one or more activity, and described sampling is that the metabolic equivalent of normalized task is measured；

Based on a determination that the value of the particular sample in described sampling is between first threshold and Second Threshold, and it was User Status the first moment interested of being classified as sitting before the particular moment in the described moment interested, and it is described User Status the second moment interested of being classified as sitting after the described particular moment in the described moment interested, the processor of described server the particular sample in the sampling of the particular moment in the described moment interested is categorized as sitting state；

Detected the multiple continuous samplings in described sampling by described processor and indicate the time period of described sitting state；

Determined that whether the described time period is more than threshold time period by described processor；And

When determining that the described time period is more than described threshold time period, user described in described processor identification it is in sitting state.

16. 1 kinds of systems, including:

Communicator, it is configured to receive the sampling of the sensor information from the moment multiple interested that wearable electronics obtains, described sensor information generates when the user associated with described wearable electronics performs one or more activity, and described sampling is that the metabolic equivalent of normalized task is measured；And

Processor, is coupled to described communicator, and described processor is configured to sampling and the predetermined threshold in comparison each described moment interested, and the sampling in each described moment interested is categorized as at least one in sitting state and non-sitting state；

Described processor is configured to the time period of the multiple continuous samplings described sitting state of instruction detecting in described sampling；

Described processor is configured to determine that whether the described time period is more than threshold time period；And

Described processor is configured to, when determining that the described time period is more than described threshold time period, identify that described user is in sitting state.

17. systems as claimed in claim 16, wherein said processor is additionally configured to generate electronic notification in response to identifying described user to be in described sitting state,

Wherein said communication device configuration is used for presenting described electronic notification in described wearable electronics for described electronic notification being sent to described wearable electronics via computer network, and wherein said electronic notification comprises user described in suggestion and terminates the message of described sitting state.

18. systems as claimed in claim 16, wherein said processor is additionally configured to determine that the value of a sampling in the sampling in one of described moment interested is less than described predetermined threshold, and wherein said processor is additionally configured in response to determining that the one sampling in described sampling is categorized as having described sitting state less than described predetermined threshold by described value.

19. systems as claimed in claim 16, each being classified as in wherein said sampling has described sitting state or described non-sitting state or sleep state.

20. systems as claimed in claim 16, each being classified as in wherein said sampling has described sitting state or described non-sitting state or the state indicating described user not dress described wearable electronics.

21. systems as claimed in claim 16, wherein said processor is configured that

Detect the multiple continuous samplings in described sampling and indicate the time period of described non-sitting state；

Determine that the plurality of continuous sampling in described sampling indicates the described time period of described non-sitting state more than predetermined amount of time；And

When the plurality of continuous sampling in determining described sampling indicates the described time period of described non-sitting state more than described predetermined amount of time, identify that described user is in non-sitting state.

22. systems as claimed in claim 16, wherein said processor is configured that the multiple continuous samplings determining in described sampling indicate the time period of described non-sitting state adjacent with the time period of the multiple continuous samplings described sitting state of instruction in described sampling,

Wherein said processor is additionally configured to: when the multiple continuous samplings in determining described sampling indicate the time period of described non-sitting state adjacent with the time period of the multiple continuous samplings described sitting state of instruction in described sampling, identify that described user is transformed into described non-sitting state from described sitting state

Wherein said communication device configuration is: when determining that described user is converted to described non-sitting state, via computer network, electronic notification being sent to described wearable electronics and be used for the display via described wearable electronics, described electronic notification comprises stimulus messages.

23. systems as claimed in claim 16, the metabolic equivalent measurement of wherein said task is the measurement of energy expenditure, each non-zero in the metabolic equivalent measurement of wherein said task.

24. systems as claimed in claim 16, wherein said processor is configured that

Determine that the value of a sampling in the sampling in one of described moment interested is more than described predetermined threshold；And

It is categorized as that there is described non-sitting state in response to determining that the one in described sampling is sampled by described value more than described predetermined threshold.

25. systems as claimed in claim 16, wherein said processor is configured that

Determine that the value of a sampling in the sampling in one of described moment interested is more than the second predetermined threshold higher than described predetermined threshold；And

It is categorized as that there is described non-sitting state in response to determining that the one in described sampling is sampled by described value more than described second predetermined threshold.

26. systems as claimed in claim 16, wherein said processor is configured that

Determining that the value of the particular sample in the sampling in moment interested more than described predetermined threshold and is less than the second predetermined threshold, described second predetermined threshold is higher than described predetermined threshold；

Determine and correspond to described sitting state prior to the value of a sampling in the sampling in the moment interested in described moment interested；

After determining, one in the sampling in the rear moment interested value sampled in the described moment interested corresponds to described sitting state；And

Described particular sample in described sampling is categorized as having described sitting state.

27. systems as claimed in claim 16, wherein said processor is configured that

Determine that the value of sampling in one group of continuous print moment interested is each more than described predetermined threshold and less than the second predetermined threshold, described second predetermined threshold is higher than described predetermined threshold；

Determine the moment interested prior to described one group of continuous print moment interested sampling in the value of a sampling corresponding to described sitting state；

In the value of one in the sampling in rear moment interested sampling in described one group of continuous print moment interested corresponding to described sitting state after determining；And

Each sampling in the described sampling in moment interested for described one group of continuous print is categorized as having described sitting state.

28. systems as claimed in claim 27, wherein said processor is configured that

Before being categorized as there is described sitting state by each sampling in the described sampling in moment interested for described one group of continuous print, determine that the size in described one group of continuous print moment interested is less than size threshold value.

29. systems as claimed in claim 16, the wherein said moment interested occurs with regular time interval.

30. 1 kinds of systems, including:

Communicator, it is configured to receive the sampling of the sensor information from the moment multiple interested that wearable electronics obtains, described sensor information generates when the user associated with described wearable electronics performs one or more activity, and described sampling is that the metabolic equivalent of normalized task is measured；

Processor, it is coupled to described communicator, described processor is configured to determine that the value of the particular sample in described sampling is between first threshold and Second Threshold, and it was User Status the first moment interested of being classified as sitting before the particular moment in the described moment interested, and it is User Status the second moment interested of being classified as sitting after the particular moment in the described moment interested, and the particular sample in the sampling of the particular moment in described moment interested is categorized as sitting state

Described processor is configured to the time period of the multiple continuous samplings described sitting state of instruction detecting in described sampling,

Described processor is configured to determine whether the described time period is more than threshold time period,

Described processor is configured to, when determining that the described time period is more than described threshold time period, identify that described user is in sitting state.