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Session: Poster Session 1 (Even numbers)
Session starts: Thursday 26 January, 16:00
Presentation starts: 16:00

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Peng Li (1. Biomedical Signals and Systems (BSS) Group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, Netherlands 2. Department of Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radb)
Richard van Wezel (1. Biomedical Signals and Systems (BSS) Group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, Netherlands 2. Department of Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radb)
Ying Wang (1. Biomedical Signals and Systems (BSS) Group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, Netherlands)

Abstract:
Introduction: Parkinson’s disease (PD) is a worldwide fast-growing neurological disorder mainly affecting the 65 and older. Healthcare professionals can only diagnose PD when patients present motor symptoms at the late disease stage according to the UK Brain Bank Criteria [1]. The individual’s disease progression is small and heterogeneous in the daily living environment which makes timely diagnosis difficult. Therefore, it is very important to track the small changes along with the disease progression during daily life to take effective interventions to prevent the disability. Telemonitoring systems with wearable sensing technology can assist healthcare professionals to gain insights into patients' daily life health conditions and help patients empower themselves to improve treatment effectiveness and mitigate disease progression [2-3]. Wrist-worn digital devices, especially smartwatches, are currently the most popular tool in the PD research field given their convenient usage during long-term daily life monitoring. Even though the telemonitoring technology of PD using wrist-worn sensing has attracted great interest with numerous articles, there is still unclarity about the value of using this technology in PD clinical practice. In this narrative review, we evaluated the challenges and opportunities of wrist-based technology in the early diagnosis, progression tracking, and self-management of PD. Methods: Peer-reviewed articles were searched in October 2022 from four databases: PubMed, Web of Science, IEEE Xplore, and Google Scholar. The search keywords are (“Parkinson” OR “PD” OR “Parkinson’s disease”) And (“wrist” OR “wrist-worn” OR “Smartwatch” OR “Fitness band”). We selected articles and extracted useful information from the articles, such as the sensors and algorithms that have been used, the features of monitoring, the monitored motor or non-motor symptoms in PD, applications in real clinical practice (early diagnosis, progression, disease management), the comfortability and usability during long time usage, etc. Results & Conclusion Further results and conclusions will be presented in the near future based on the findings from the literature review. Reference: 1. Calne DB, Snow BJ, Lee C. Criteria for diagnosing Parkinson’s disease. Ann Neurol (1992) 32:S125–7. 10.1002/ana.410320721 2. Dorsey ER, et al. “Moving Parkinson care to the home”. Movement Disorders. 2016 Sep;31(9):1258-1262. DOI: 10.1002/mds.26744. 3. Dorsey, E Ray, and Eric J Topol. “Telemedicine 2020 and the next decade.” Lancet (London, England) vol. 395,10227 (2020): 859. doi:10.1016/S0140-6736(20)30424-4 * Corresponding author: Peng Li, pli-1@utwente.nl