{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T06:08:17Z","timestamp":1773036497222,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000},"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>The growing aging of the world population is leading to an aggravation of diseases, which affect the autonomy of the elderly. Wireless body sensor networks (WBSN) are part of the solutions studied for several years to monitor and prevent loss of autonomy. The use of optical wireless communications (OWC) is seen as an alternative to radio frequencies, relevant when electromagnetic interference and data security considerations are important. One of the main challenges in this context is optical channel modeling for efficiently designing high-reliability systems. We propose here a suitable optical WBSN channel model for tracking the elderly during a walk. We discuss the specificities related to the model of the body, to movements, and to the walking speed by comparing elderly and young models, taking into account the walk temporal evolution using the sliding windowing technique. We point out that, when considering a young body model, performance is either overestimated or underestimated, depending on which windowing parameter is fixed. It is, therefore, important to consider the body model of the elderly in the design of the system. To illustrate this result, we then evaluate the minimal power according to the maximal bandwidth for a given quality of service.<\/jats:p>","DOI":"10.3390\/s21092904","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T21:25:10Z","timestamp":1619040310000},"page":"2904","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Channel Modeling of an Optical Wireless Body Sensor Network for Walk Monitoring of Elderly"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5644-0732","authenticated-orcid":false,"given":"Alassane","family":"Kaba","sequence":"first","affiliation":[{"name":"XLIM Laboratory, UMR CNRS 7252, University of Limoges, 87000 Limoges, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4238-9136","authenticated-orcid":false,"given":"Stephanie","family":"Sahuguede","sequence":"additional","affiliation":[{"name":"XLIM Laboratory, UMR CNRS 7252, University of Limoges, 87000 Limoges, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1141-2874","authenticated-orcid":false,"given":"Anne","family":"Julien-Vergonjanne","sequence":"additional","affiliation":[{"name":"XLIM Laboratory, UMR CNRS 7252, University of Limoges, 87000 Limoges, France"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1109\/JBHI.2016.2539098","article-title":"Fall Risk Assessment Through Automatic Combination of Clinical Fall Risk Factors and Body-Worn Sensor Data","volume":"21","author":"Greene","year":"2017","journal-title":"IEEE J. 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