{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:54:12Z","timestamp":1783612452947,"version":"3.55.0"},"reference-count":27,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T00:00:00Z","timestamp":1637712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sensor placement identification in body sensor networks is an important feature, which could render such a system more robust, transparent to the user, and easy to wear for long term data collection. It can be considered an active measure to avoid the misuse of a sensing system, specifically as these platforms become more ubiquitous and, apart from their research orientation, start to enter industries, such as fitness and health. In this work we discuss the offline, fixed class, sensor placement identification method implemented in PDMonitor\u00ae, a medical device for long-term Parkinson\u2019s disease monitoring at home. We analyze the stepwise procedure used to accurately identify the wearables depending on how many are used, from two to five, given five predefined body positions. Finally, we present the results of evaluating the method in 88 subjects, 61 Parkinson\u2019s disease patients and 27 healthy subjects, when the overall average accuracy reached 99.1%.<\/jats:p>","DOI":"10.3390\/s21237801","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"7801","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Configurable Offline Sensor Placement Identification for a Medical Device Monitoring Parkinson\u2019s Disease"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2444-1962","authenticated-orcid":false,"given":"Nicholas","family":"Kostikis","sequence":"first","affiliation":[{"name":"PD Neurotechnology Ltd., London EC2N 1ER, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5102-6185","authenticated-orcid":false,"given":"George","family":"Rigas","sequence":"additional","affiliation":[{"name":"PD Neurotechnology Ltd., London EC2N 1ER, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Spyridon","family":"Konitsiotis","sequence":"additional","affiliation":[{"name":"Department of Neurology, Medical School, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7362-5082","authenticated-orcid":false,"given":"Dimitrios I.","family":"Fotiadis","sequence":"additional","affiliation":[{"name":"Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8553","DOI":"10.1109\/JIOT.2019.2920283","article-title":"IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment","volume":"6","author":"Bianchi","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Eyobu, E.O., and Han, D. 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