Abstract
Schizophrenia is a mental illness characterized by relapsing episodes of psychosis. Schizophrenia is treatable, and treatment with medicines and psychosocial support is effective. However, schizophrenia is one of the most expensive mental illnesses in terms of total medical costs required, including costs for effective treatment and for the continuous support and monitoring that is necessary. It is therefore useful and beneficial to explore how new technology, such as dialogue systems and social robots, can be used to provide help and assistance for care personnel as well as for the patients in the treatment and recovery from the illness. In this paper, we discuss various issues related to the development of a dialogue system that is able to recognize the characteristics of schizophrenia and provide support for schizophrenia patients 24 h a day.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Baer JS, Wells EA, Rosengren DB, Hartzler B, Beadnell B, Dunn C (2009) Agency context and tailored training in technology transfer: a pilot evaluation of motivational interviewing training for community counselors. J Subst Abuse Treat 37(2):191–202
Billard A, Robins B, Nadel J, Dautenhahn K (2006) Building Robota, a mini-humanoid robot for the rehabilitation of children with autism. RESNA Assist Technol J
Brenner HD et al (1996) Integrated Psychological Therapy for Schizophrenic Patients. Hogrefe and Huber, Gottingen
Coninx A et al (2015) Towards long-term social child-robot interaction: using multi-activity switching to engage young users. J Hum-Rob Interact
Goldberg TE et al (1987) Further evidence for dementia of the prefrontal type in schizophrenia? a controlled study of teaching the Wisconsin Card Sorting test. Arch Gen Psychol 44:1008–1014
Grasemann U, Hoffman R, Miikkulainen R (2011) Modeling acute and compensated language disturbance in schizophrenia. In: Proceedings of the 33rd Annual Meeting of the Cognitive Science Society. http://www.cs.utexas.edu/users/ai-lab?grasemann:cogsci11
Green MF (1996) What are the functional consequences of neurocognitive deficits in schizophrenia? Am J Psychiatry 153:321–30
Heinrichs RW, Zakzanis KK (1998) Neurocognitive deficit in schizophrenia: a quantitative review of the evidence. Neuropsychology 12(3):426–445
Jokinen K (2018) Dialogue models for socially intelligent robots. In: Ge S, Cabibihan J, Salichs M, Broadbent E, He H,Wagner A, Castro-González A (eds) Social Robotics. ICSR 2018, pp 127–138. Springer, Heidelberg
Jokinen K, Fukuda K, Iino N, Nishimura S, Nishimura T, Oota Y, Watanabe K, Yoshida Y (2019) Ethical and privacy issues in interactive service applications for elder people concerning dialogues and social robots. In: 7th Serviceology Conference, Tokyo
Jokinen K, Nishimura S, Watanabe K, Nishimura T (2019) Human-robot dialogues for explaining activities. In: Fernando D’Haro L, Banchs R, Li H (eds) 9th International Workshop on Spoken Dialogue Systems, pp 239–251. Springer, Heidelberg
Jokinen K, Watanabe K (2019) Boundary-crossing robots: societal impact of interactions with socially capable autonomous agents. In: Salichs H, Ge SS, Barakova EI, Cabibihan JJ, Wagner AR, Castro-González Á, He H (eds) Social Robotics, pp 3–13. Springer, Heidelberg
Kruijff-Korbayová I et al (2015) Young users’ perception of a social robot displaying familiarity and eliciting disclosure. In: Tapus A et al (eds) Proceedings of ICSR 2015, LNAI 9388, pp 380–389. Springer, Heidelberg
Manzeschke A, Weber K, Rother E, Fangerau H (2015) Ethical questions in the area of age appropriate assisting systems
Marshall IJ, Kuiper J, Wallace BC (2016) RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials. Neuropsychology 23(1):193–201
Pérez-Rosas V, Mihalcea R, Resnicow K, Singh S, An L, Goggin KJ, Catley D (2017) Predicting counselor behaviors in motivational interviewing encounters. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, vol 1, pp 1128–1137
Tanana M, Hallgren KA, Imel ZE, Atkins DC, Srikumar V (2016) A comparison of natural language processing methods for automated coding of motivational interviewing. J Subst Abuse Treat 65:43–50
Wexler B, Hawkins K, Rounsaville B, Anderson M, Sernyak M, Green M (1997) Normal neurocognitive performance after extended practice in patients with schizophrenia. Schizophr. Res. 26(2–3):173–180
WHO: Schizophrenia (2017). https://www.who.int/news-room/fact-sheets/detail/schizophrenia
Wykes T (2000) Cognitive rehabilitation and remediation in schizophrenia. In: Sharma T, Harvey PD (eds) Cognition in schizophrenia: Impairments, importance, and treatment strategies. Oxford University Press, Oxford
Acknowledgements
The work is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Oshiyama, C., Niwa, Si., Jokinen, K., Nishimura, T. (2021). Development of a Dialogue System that Supports Recovery for Patients with Schizophrenia. In: D'Haro, L.F., Callejas, Z., Nakamura, S. (eds) Conversational Dialogue Systems for the Next Decade. Lecture Notes in Electrical Engineering, vol 704. Springer, Singapore. https://doi.org/10.1007/978-981-15-8395-7_16
Download citation
DOI: https://doi.org/10.1007/978-981-15-8395-7_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8394-0
Online ISBN: 978-981-15-8395-7
eBook Packages: EngineeringEngineering (R0)