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WO2019029777A1 - Nouvelle interface neuronale motrice cyberorganique - Google Patents

Nouvelle interface neuronale motrice cyberorganique Download PDF

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Publication number
WO2019029777A1
WO2019029777A1 PCT/DK2018/050186 DK2018050186W WO2019029777A1 WO 2019029777 A1 WO2019029777 A1 WO 2019029777A1 DK 2018050186 W DK2018050186 W DK 2018050186W WO 2019029777 A1 WO2019029777 A1 WO 2019029777A1
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WO
WIPO (PCT)
Prior art keywords
signal
muscle
component
contraction
neural
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Application number
PCT/DK2018/050186
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English (en)
Inventor
Emil BÜLOW PEDERSEN
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Buelow Pedersen Emil
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Application filed by Buelow Pedersen Emil filed Critical Buelow Pedersen Emil
Publication of WO2019029777A1 publication Critical patent/WO2019029777A1/fr

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4519—Muscles
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0261—Strain gauges
    • A61B2562/0266—Optical strain gauges
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/12—Manufacturing methods specially adapted for producing sensors for in-vivo measurements

Definitions

  • the invention relates to the field of brain-computer interfaces - that is, extracting intention from the nervous system of a human or animal. It is targeted towards the collection of motor data from the human nervous system. It aims to solve the major problems of the low resolution, low bandwidth neural interfaces that are currently used in bionics, neuroprosthetics and gesture control devices.
  • the invention comprises a novel motor-neural brain -computer interface for extracting intended and or actual movement from a human or animal.
  • the interface converts motor-neural signals to intended contraction data of the recipient muscle. This is accomplished using a pipeline of organic, mechanic and electronic components each responsible for translating the neural signals to intermediate representations that are eventually converted to a digital or analog signal describing the contraction value encoded in the original neural signal.
  • Known methods of extracting intended movement data from the motor-neural system include but are not limited to:
  • Electromyography which measures the average action potential over an area of axons or muscle tissue, works by letting the electrical field caused by the polarization changes during action potential in the axons of motor units induce a voltage between two electrodes placed in close proximity to the measured tissue. This voltage is then amplified, rectified, and filtered. Even at this point, the signal mostly looks like noise. Generally, the amount of noise is seen as an indicator of intended contraction strength. Not only is this not a very precise measure of intended contraction - the signal varies greatly between muscle activation, sustained contraction and with fatigue, making it even harder to accurately predict the intended contraction value of a particular muscle. Furthermore, surface EMG requires direct skin contact, whereas deep EMG requires electrodes to be directly exposed to the measured tissue. This last connection can degrade considerably over time due to the formation of scar tissue around the electrodes of the system.
  • Electrocorticography fundamentally works in the same way as EMG. The main difference is that the electrodes are placed directly onto the surface of the brain, and that they are usually packed into arrays of many ECG channels. The outputs of these channels are combined using machine learning algorithms that are trained to recognize the activation patterns associated with different movements. While this works relatively well, it is highly invasive and requires a long period of training prior to efficient use.
  • Electroencephalography works by electrically detecting neural activity noninvasive ⁇ through the scalp in a fashion similar to EMG. It contains many electrodes combined using the same types of algorithms and training used for ECG. One major difference is that EEG electrodes are placed all over the scalp, meaning that they get signals from many different areas of the brain. The result is that abstract brain centers are suddenly responsible for performing movement related tasks in addition to their original tasks. This, coupled with the poor signal quality from measuring through the scalp, results in very poor bandwidth and a high need for attention from the user.
  • Myooximetry works by detecting the amount of oxygenated blood within a muscle. During contraction, this amount increases to meet the increased oxygen and energy demand of the muscle. This is also not a very accurate measure of muscle contraction, since it suffers from many of the same problems as EMG. Furthermore, the delay from activation to increased blood flow is non -negligible.
  • Electrogoniometers are essentially protractors for skeletal joints. They require the entire joint to be present, and furthermore requires the placing of sensing elements on both members of the joint. This makes it next to useless for extracting intention in bionics, neuroprosthetics and consumer products where required limbs and tissue may be unavailable for various reasons. Switches, accelerometers, pressure sensors and other "traditional" controls have traditionally been used as a primitive way to control simple prostheses. Recently, through the work of DEKA, more advanced control schemes have been developed, yet a common problem persists: A separate limb is needed in order to operate these types of controls. This is often seen in the shape of a pressure sensor in the shoe or an accelerometer on the shoulder. Controlling the bionic limb thus interferes with the normal operation of the controlling limb, making it unfavorable for seamless integration with the nervous system.
  • the present invention eliminates most of these problems:
  • it can allow direct 1 : 1 mapping of intended contraction of each muscle in a given amputated limb to actual movement in a bionic replacement limb.
  • the high resolution and low latency are the key elements of the invention that sets it apart from the known art.
  • the feedback loop between the body ' s senses and the movement of the bionic limb can be made small enough for the patient ' s motor cortex to perform real time corrections across a large number of cooperating muscles.
  • the motor cortex can be likened to the difference between rendering 3D graphics on a CPU versus rendering on a GPU - that is, the motor cortex is a highly specialized center capable of performing complex movements at a far lower attention cost than other parts of the brain. This invention allows bionics to tap into that power.
  • US patent US 5, 134,281 (Bryenton et al) discloses an optical deformation sensor based on an optical fiber with a light source in one end and a light detector in the other.
  • the optical fiber is arranged in a micro bend pattern, such that "accordion expansion or contraction" of the sensor results in a decrease in light intensity proportional to the degree of stretching - ie. the physical phenomenon measured by the sensor is changes in its length.
  • the patent discloses embodiments for monitoring vital signs of humans, such as pulse, breathing or other physiological activity. Since the sensor relies on stretching, the deformation caused by physiological phenomena must be transferred to the sensing component in a fashion that stretches it.
  • the solutions described in the patent include taping the sensor directly to the skin, or tying it around the body part being measured, such that a change in circumference of the body part being measured results in the sensor being stretched.
  • US5633494A1 discloses a curvature sensor consisting of an elongated deformable member, onto which an optical fiber is fixed with a light source in one e and a light detector in the other.
  • a region of the fiber is configured to be light absorbing, such that bending of the fiber results in a decrease in light intensity.
  • W09841815A1 discloses an optical deformation sensor based on the previously described prior art from US5633494A1 by the same inventor. Several instances of the therein disclosed sensors are placed along a ribbon like structure, allowing the spatial configuration of the ribbon to be computed.
  • the ribbon is used for 3D motion capture for use in animation.
  • the ribbon is spun around the limb being captured in a helix like fashion. This allows the device to capture the volumetric changes of the limb during joint bending.
  • bending artifacts were a great problem in animation skinning, and could not be solved effectively using computer algorithms.
  • the interface converts motor-neural signals to electrical analog or digital signals representing desired muscle contraction levels encoded in the neural signal. It does so by using muscle tissue as an intermediary translator, allowing the desired contraction level to be decoded as mechanical deformation.
  • the flow of information looks as such:
  • a move is planned in the central nervous system.
  • the mechanical signal is transformed to an electrical signal by a muscle curvature sensing component.
  • the electrical signal is filtered, processed and if needed, converted to a digital signal, which describes the contraction level encoded in the neural signal.
  • the analog or digital signal is used in an application. This may include combining multiple interface instances, in order to extract more complex intended actions.
  • Points 3, 4, and 5 combined form a direct cyber-organic motor-neural interface. When modeled as such, a neural signal enters one end of the interface, and an analog or digital signal describing the decoded level of contraction exits the other.
  • Point 3 is referred to as "The organic component”
  • point 4 is referred to as “The mechanical component”
  • point 5 is referred to as "The signal processing component”.
  • the mechanical component can optionally be connected non-invasively, by letting the skin act as a mechanical transmitter between the organic and mechanical components. Due to the imperfect mechanical transmission of the skin coupled with the fact that it may receive mechanical signals from multiple sources, this mode of operation often sees a fair amount of motion artifacts present in the output signal. These are often caused by mechanical interference from nearby muscles but can also be caused by nearby joints. This can be mitigated by combining multiple interface instances using machine learning or sensor fusion algorithms. It can even be combined with other types of neural interfaces like e.g. electromyography. In this case, EMG would be used to correlate the deformation of muscle tissue with the activation of the nerve that innervates it, in order to verify that the detected deformation did in fact originate in the measured tissue.
  • electromyography e.g. electromyography
  • TMR targeted muscle reinnervation
  • the mechanical component is affixed on, near or within the muscle tissue of the organic component. It transforms the mechanical signals generated by contracting muscle tissue into an electrical signal, which can be further processed for use in an application.
  • the sensing component relies on the fact that the stiffness and diameter of a skeletal muscle changes as the muscle contracts. As such, the spatial deformation between a series of points in a muscle is proportional to its contraction level.
  • the mechanical component in its most basic form consists of a deformable member onto which a photo-emitter and a photo-detector are placed in such a way that the light hits the photo-detector at maximum intensity, when the deformable member is in its resting position.
  • the resting position is defined as the state in which the deformable member experiences the least degree of deformation while being fully mechanically compliant with the organic component onto which it is placed.
  • part of the deformable member is stiffened as shown in drawing 3.
  • the stiff part casts a shadow onto the photo-emitter, creating a much sharper divide between light and dark. This results in the mechanical component being much more sensitive to minute deformations, making it more suited for detecting the tiny deformations of some embodiments of the organic component.
  • This embodiment is particularly well suited to manufacturing using flexible printed circuits (FPCs).
  • the deformable member is the flexible circuit itself with appropriate stiffening
  • the photo-emitter is a side mounted SMD LED
  • the photo-detector is a side-mounted SMD photoresistor, phototransistor or reverse biased LED.
  • one or more opaque blockades are placed along the deformable member as shown in drawing 4.
  • the blockade casts a shadow onto the photo-emitter, creating a much sharper divide between light and dark. This results in the mechanical component being much more sensitive to minute deformations, making it more suited for detecting the tiny deformations of some embodiments of the organic component. This embodiment is especially sensitive to deformations that occur close to the blockade (s) along the deformable member.
  • the present invention can help greatly with regaining proper movement after neuropathy or brain damage.
  • the patient can more effectively identify actions that result in contraction, helping them regain control of muscles and stimulating a higher rate of nerve regrowth.
  • the interface can be used as input for computer games, allowing patients to have fun while performing otherwise very monotonous and repetitive tasks. This has been shown to increase patient retention rates and speed up recovery.
  • the mental wellbeing of patients is known to be an important aspect of the recovery process.
  • the interface provides valuable data for doctors and therapists to track progress.
  • the present invention can help greatly with rehabilitating muscles after injuries or medical operations.
  • exercises can be controlled and monitored accurately by therapists.
  • this allows patients to have fun while performing otherwise very monotonous and repetitive tasks. This has been shown to increase patient retention rates and speed up recovery.
  • the mental wellbeing of patients is known to be an important aspect of the recovery process.
  • a perineometer can be constructed by configuring the invention to use the perineum as the organic component and optimizing the mechanical component to its long muscles and minute deformations during contraction. This allows a far more comfortable and non-invasive approach to treatment of urinary incontinence for both male and female patients.
  • the present invention is able to log the exact contraction value of tremors at high sample rates and high resolution. In addition to more accurate data collection over time, this will also allow researchers to study and compare the waveforms of individual contractions. This will allow researchers to more accurately study neurodegenerative diseases as well as study the effect of various drugs and treatments on the nervous system of the patient.
  • a great problem in neuromuscular prosthetics is determining the intention of the user. Almost most joints are actuated by more than one muscle, so even if one becomes impaired, its movement can often be approximated by looking at the contraction values of one or more of the other muscles. An example of how this would work can be seen with drop foot. In patients with drop foot the tibialis anterior muscle is paralyzed, leaving the patient unable to lift their foot, resulting in an abnormal, often unhealthy gait. In this situation, the contraction levels of the calf muscles can be used to predict the patient ' s intent and estimate the desired contraction value of the tibialis anterior muscle, which can in turn be stimulated accordingly.
  • the contraction level resulting from the neural commands sent by the artificial muscle stimulator can be known with very little delay. This level can in turn be compared to the desired contraction level, and the stimulation signal can be adjusted accordingly. This allows any contraction level to be induced and held, and even allows the desired contraction level to change over time, resulting in smooth continuous artificially stimulated movement.
  • bionics One of the most important aspects of bionics is allowing the patient to intuitively control the bionic limb. This means that the bionic limb must be able to extract or estimate the intentions of the patient. As previously described, most presently known methods of control provide low bandwidth, slow operation and require a significant amount of attention from the patient.
  • the present invention allows decoding of intended contraction values for muscles with very high resolution and low delay, it is a prime candidate for the job.
  • Some types of amputation such as transradial and transtibial amputations allow most of the muscles controlling the amputated limb to persist in the stump post amputation.
  • the neural interface can be configured to use the remaining muscle tissue as the organic component. In the non-invasive embodiments, this allows the creation of inexpensive, mass produced bionic limbs.
  • the interface can be implanted in order to have perfect placement of the interface every time without effort from the patient.
  • the neural interface In cases where more degrees of freedom are desired, or where the type of amputation doesn ' t leave as much usable muscle tissue, the neural interface must be configured to use surgically attached muscle tissue as the organic component. By combining multiple interface instances, this allows 1 :1 mapping of the desired contraction of each muscle in the amputated limb to actual movement in a bionic limb.
  • Drawing 1 illustrates the pipeline that decodes motor neural signals, outputting a digital or analog value describing the contraction level encoded in the neural signal.
  • the command to contract the muscle 4 begins in the central nervous system 1 . It is transmitted through multiple motor neurons 2 and their axons 3, each innervating multiple muscle fibers of the organic component 4, When each muscle fiber receives an action potential it contracts, resulting in mechanical deformation of the organic component.
  • This mechanical signal is transmitted to the compliant mechanical component 5, which converts the mechanical signal to an analog electrical signal of proportional intensity.
  • This signal is optionally converted to a digital signal and optionally further processed by a computer 6,
  • the resulting digital signal is an absolute representation of the intended contraction level originally encoded in the signal sent by the central nervous system.
  • Drawing 2 illustrates the basic construction of the mechanical component.
  • Drawing 2. a is a side view in the resting position
  • Drawing 2.b is a side view in a curved position
  • Drawing 2,c is a top down view in the resting position
  • the mechanical component consists of a deformable member 1 onto which a photo- emitter 2 is placed such that the primary direction of the emitted light points directly towards a photo-detector 3, which is pointed directly towards the emitter when the deformable member 1 is in its default resting position.
  • the light from 2 hits 3 at maximum intensity.
  • the fraction of photons from the photo-emitter 2, that successfully enter the photo- detector 3 is decreased in proportion to the degree of deformation of the deformable member 1 .
  • Drawing 3 illustrates an embodiment of the mechanical component from drawing 2 which is more sensitive to minute deformations.
  • parts 4 of the deformable member 1 have been stiffened, such that they do not deform as easily as the rest of the member 1.
  • the result is that when light from the light source 2 hits the stiffened parts 4, the resulting shadows of the stiffened parts 4 have their penumbras projected upon the photo-detector 3, such that there is a much sharper transition from light to dark.
  • This allows the output signal to be much more sensitive, meaning that it can be calibrated such that even tiny deformations completely saturate the sensing component.
  • the stiffened parts 4 do not need to be present in both sides, and their length can be varied to vary the active mechanical band of the component, in order to calibrate it to different types and sizes of the organic component.
  • Drawing 4 illustrates an embodiment of the mechanical component from drawing 2 which is more sensitive to minute deformations.
  • one or more opaque blockades 4 are placed onto the deformable member 1 , such that its movement is perpendicular to the direction of the light emitted from the light emitter 2.
  • the blockade casts a shadow onto the photo- detector in a way such that the position and width of the penumbra can be controlled by varying the height and position of the blockade, respectively.
  • the blockade (s) 4 moves relative to the photo-emitter 2 and photo-detector 3, such that the intensity of light detected by 3 is proportional to the deformation of the deformable member 1.
  • Drawing 5 illustrates the orientation of the mechanical component relative to the muscle fibers of the organic component. It shows several different scenarios, all of which are valid orientations.
  • the mechanical component 2 is placed on, in or near the organic component 1 , in such a way that mechanical deformation of the organic component 1 will be transferred to the mechanical component 2.
  • the drawing serves to illustrate that any orientation is valid, as long as the organic component 1 experiences adequate mechanical deformation in the plane of deformation of the mechanical component 2.
  • Drawing 6 illustrates an embodiment wherein the organic component is one of the patient ' s existing skeletal muscles.
  • nn tthhiiss eemmbbooddiimmeenntt tthhee mmeecchhaanniiccaall ccoommppoonneenntt 22
  • iiss ppllaacceedd ddiirreecctt llyy aabboovvee tthhee ddeessiirreedd mmuussccllee iinn tthhee lliimmbb 11
  • Drawing 7 illustrates an embodiment wherein the organic component is one of the patient ' s existing skeletal muscles, and wherein the mechanical component is surgically implanted.
  • the mechanical component 2 is implanted near or in the desired muscle in the limb 1 , in such a way that contraction of the muscle results in deformation of the mechanical component 2,
  • the output of the mechanical component 2 is fed to an implanted computer 3, containing a means of wireless power reception and a transmitter for communicating the digital representation of the original neural signal to an external receiver not shown in the drawing.
  • the computer 3 receives power wirelessly from an external wireless power transmitter located in the near vicinity of the power receptor of the computer 3.
  • Drawing 8 illustrates an embodiment wherein the nerve whose signal is to be decoded has been damaged or transected. This means that no existing muscle tissue can be used as the organic component without prior modification.
  • the damaged nerve 2 in the limb 1 (shown here as the stump of an amputated limb) is surgically attached to the organic component 3 at the transection site 5 using targeted muscle rein nervation (TMR),
  • TMR targeted muscle rein nervation
  • the muscle tissue that makes up the organic component 3 can come from a variety of sources, including but not limited to muscle tissue grown from scratch, grafted from elsewhere in the body, or tissue still located elsewhere in the body, in which case the nerve 2 must be rerouted to reach said tissue.
  • the mechanical component 4 is attached to the surface of the organic component 3 either by attaching the ends of the elongated member of the mechanical component 4 to the tendons at the ends of the organic component 3, or by attaching the mechanical component 4 to an elastic biocompatible pouch encapsulating the organic component 3.
  • Drawing 9 illustrates extraction of complex motor data by combining multiple interfaces
  • Drawing 10 illustrates an embodiment that allows precise closed loop contraction control during stimulation of muscles.
  • An artificial muscle stimulator 1 containing a means of neural stimulation 2, stimulates the nerve 3 of a muscle 4, which acts as the organic component of the neural interface.
  • the exact method of stimulation is unimportant, as long as the intensity of the stimulation can be modulated in real time.
  • the resulting contraction is transmitted to the mechanical component 5, which converts the contraction to an electrical signal , which is transmitted back to the artificial muscle stimulator 1 .
  • the actual contraction level is then compared to the desired contraction level by 1 , and the stimulation signal is modulated according to the algorithm described in drawing 1 1 .
  • Drawing 11 is an embodiment that allows precise closed loop contraction control during stimulation of muscles.
  • Drawing 1 1 is a diagram illustrating the algorithm used to modulate the stimulation signal of the embodiment described in drawing 10. it is the same algorithm as used in servo motors, and it is shown here in its simplest form.

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Abstract

L'invention comprend une nouvelle interface cerveau neuronal/machine motrice cyberorganique dans le but d'extraire un mouvement souhaité et/ou réel d'un être humain ou d'un animal. L'interface convertit des signaux neuronaux moteurs en données de contraction prévues du muscle récepteur. Ceci est réalisé à l'aide d'un pipeline de composants organiques, mécaniques et électroniques, chacun responsable de la traduction des signaux neuronaux en représentations intermédiaires qui sont finalement converties en un signal numérique ou analogique décrivant la valeur de contraction codée dans le signal neuronal d'origine. La commande de contraction d'un muscle commence dans le système nerveux central (1). Elle est transmise à travers de multiples neurones moteurs (2) et leurs axones (3), chacun innervant de multiples fibres musculaires du composant organique (4). Chaque fibre se contracte lors de la réception d'un potentiel d'action, conduisant à un signal mécanique. Ce signal est transmis au composant mécanique conforme (5), qui convertit le signal mécanique en un signal électrique analogique d'intensité proportionnelle.
PCT/DK2018/050186 2017-08-07 2018-07-20 Nouvelle interface neuronale motrice cyberorganique WO2019029777A1 (fr)

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DKPA201700437A DK181212B1 (en) 2017-08-07 2017-08-07 A bidirectional brain-computer interface based on a novel optical sensor that measures muscle contractions through changes in surface topology
DKPA201700437 2017-08-07

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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4414537A (en) * 1981-09-15 1983-11-08 Bell Telephone Laboratories, Incorporated Digital data entry glove interface device
US5134281A (en) * 1990-01-31 1992-07-28 E.L. Bryenton & Associates Inc. Microbend optic sensor with fiber being sewn thereto in a sinuously looped disposition
WO1994029671A1 (fr) * 1993-06-10 1994-12-22 Danisch Lee A Detecteur de courbure et de positionnement a fibre optique
US5633494A (en) * 1991-07-31 1997-05-27 Danisch; Lee Fiber optic bending and positioning sensor with selected curved light emission surfaces
WO1998041815A1 (fr) * 1997-03-17 1998-09-24 Canadian Space Agency Instrument servant a mesurer la topologie et le mouvement
GB2329243A (en) * 1997-09-05 1999-03-17 Univ Portsmouth Enterprise Optical force sensor for forces applied to the body
US20110292049A1 (en) * 2010-05-25 2011-12-01 Glenn Muravsky Motion sensor
WO2017210648A1 (fr) * 2016-06-02 2017-12-07 Woodbury Mark B Capteur de flexion à lumière directe

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4414537A (en) * 1981-09-15 1983-11-08 Bell Telephone Laboratories, Incorporated Digital data entry glove interface device
US5134281A (en) * 1990-01-31 1992-07-28 E.L. Bryenton & Associates Inc. Microbend optic sensor with fiber being sewn thereto in a sinuously looped disposition
US5633494A (en) * 1991-07-31 1997-05-27 Danisch; Lee Fiber optic bending and positioning sensor with selected curved light emission surfaces
WO1994029671A1 (fr) * 1993-06-10 1994-12-22 Danisch Lee A Detecteur de courbure et de positionnement a fibre optique
WO1998041815A1 (fr) * 1997-03-17 1998-09-24 Canadian Space Agency Instrument servant a mesurer la topologie et le mouvement
GB2329243A (en) * 1997-09-05 1999-03-17 Univ Portsmouth Enterprise Optical force sensor for forces applied to the body
US20110292049A1 (en) * 2010-05-25 2011-12-01 Glenn Muravsky Motion sensor
WO2017210648A1 (fr) * 2016-06-02 2017-12-07 Woodbury Mark B Capteur de flexion à lumière directe

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