Abstract
Motor peculiarity is an integral part of the schizophrenia disorder, having various manifestations both throughout the phases of the disease, and as a response to treatment. The current subjective non-quantitative evaluation of these traits leads to multiple interpretations of phenomenology, which impairs the reliability and validity of psychiatric diagnosis. Our long-term objective is to quantitatively measure motor behavior in schizophrenia patients, and develop automatic tools and methods for patient monitoring and treatment adjustment. In the present study, wearable devices were distributed among 25 inpatients in the closed wards of a Mental Health Center. Motor activity was measured using embedded accelerometers, as well as light and temperature sensors. The devices were worn continuously by participants throughout the duration of the experiment, approximately one month. During this period participants were also clinically evaluated twice weekly, including patients’ mental, motor, and neurological symptom severity. Medication regimes and outstanding events were also recorded by hospital staff. Below we discuss the general framework for monitoring psychiatric patients with wearable devices. We then present results showing correlations between features of activity in various daily time-windows, and measures derived from the psychiatrist’s clinical assessment or abnormal events in the patients’ routine.
Published in Proc. of 7th EAI International Conference on Wireless Mobile Communication and Healthcare (MobiHealth), Nov 2017, Vienna Austria.
T. Tron and Y.S. Resheff contributed equally to this work.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Tron, T., Resheff, Y.S., Bazhmin, M., Peled, A., Weinshall, D. (2018). Real-Time Schizophrenia Monitoring Using Wearable Motion Sensitive Devices. In: Perego, P., Rahmani, A., TaheriNejad, N. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-98551-0_28
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DOI: https://doi.org/10.1007/978-3-319-98551-0_28
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