{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T05:08:31Z","timestamp":1772168911465,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:00:00Z","timestamp":1759449600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000024","name":"the Canadian Institutes for Health Research","doi-asserted-by":"crossref","award":["CIHR-495614"],"award-info":[{"award-number":["CIHR-495614"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"crossref"}]},{"name":"The Quebec Parkinson Network (QPN), Parkinson Quebec, and Canada Research Chair in Biomechanical Movement Signature to Better Understand and Reduce Disability","award":["#CRC-2019-00214"],"award-info":[{"award-number":["#CRC-2019-00214"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>Background. Automatic detection of Sit phases in people with Parkinson\u2019s disease (PD) using a single body-worn sensor is crucial for enhancing long-term, home-based monitoring of mobility. Aim. The aim of this study was to enhance the accuracy of detecting and segmenting Sit phases in people with PD using a single SmartWatch worn at the ankle. Method. Twenty-two patients living with PD performed activities of daily living that incorporate repeated transitions to a seated position in a simulated free-living environment during 3 min, 4 min, and 5 min trials. Tri-axial accelerations and angular velocities of the right or left ankle were recorded at 50 Hz using a SmartWatch. Random forest algorithms were trained using raw and filtered data to automatically detect and segment Sit phases. Sensibility, specificity, and F-scores were calculated based on manual segmentation using the OptiTrack motion capture system. Results. Sensibility, specificity, and F-score achieved 78.3%, 93.8%, and 84.7% for Sit phase detection of the 3 min trial; 78.8%, 85.5%, and 80.6% for Sit phase detection of the 4 min trial; and 71.6%, 84.8%, and 75.6% for Sit phase detection of the 5 min trial. The median time difference between the manual and automatic segmentation was 0.95s, 0.89s, and 0.84s, respectively, for the 3 min, 4 min, and 5 min trial. Conclusion. This study demonstrates that a random forest algorithm can accurately detect and segment Sit phases in people with PD using data from a single ankle-worn SmartWatch. The algorithm\u2019s performance was comparable to manual segmentation, while substantially reducing the time and effort required. These findings represent a meaningful step forward in enabling efficient, long-term, and home-based monitoring of mobility and symptom progression in people with PD.<\/jats:p>","DOI":"10.3390\/s25196104","type":"journal-article","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T07:57:27Z","timestamp":1759478247000},"page":"6104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhanced Detection and Segmentation of Sit Phases in Patients with Parkinson\u2019s Disease Using a Single SmartWatch and Random Forest Algorithms"],"prefix":"10.3390","volume":"25","author":[{"given":"Etienne","family":"Goubault","sequence":"first","affiliation":[{"name":"Institut de Recherche Robert-Sauv\u00e9 en Sant\u00e9 et en S\u00e9curit\u00e9 du Travail (IRSST), 505 Boul. de Maisonneuve O, Montr\u00e9al, QC H3A 3C2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Camille","family":"Martin","sequence":"additional","affiliation":[{"name":"Centre de Recherche sur le Vieillissement, CIUSSS de l\u2019Estrie\u2014CHUS, Sherbrooke, QC J1H 4C4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5906-1041","authenticated-orcid":false,"given":"Christian","family":"Duval","sequence":"additional","affiliation":[{"name":"D\u00e9partement des Sciences de l\u2019Activit\u00e9 Physique, Universit\u00e9 du Qu\u00e9bec \u00e0 Montr\u00e9al, Montr\u00e9al, QC H2X 1Y4, Canada"},{"name":"Centre de Recherche de l\u2019Institut Universitaire de G\u00e9riatrie de Montr\u00e9al, Montr\u00e9al, QC H3W 1W6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Fran\u00e7ois","family":"Daneault","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation and Movement Sciences, Rutgers University, Newark, NJ 07107, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6582-4519","authenticated-orcid":false,"given":"Patrick","family":"Boissy","sequence":"additional","affiliation":[{"name":"Centre de Recherche sur le Vieillissement, CIUSSS de l\u2019Estrie\u2014CHUS, Sherbrooke, QC J1H 4C4, Canada"},{"name":"D\u00e9partement de Chirurgie, Service d\u2019Orthop\u00e9die, Facult\u00e9 de M\u00e9decine et des Sciences de la Sant\u00e9, Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1H 5H3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5409-7130","authenticated-orcid":false,"given":"Karina","family":"Lebel","sequence":"additional","affiliation":[{"name":"Centre de Recherche sur le Vieillissement, CIUSSS de l\u2019Estrie\u2014CHUS, Sherbrooke, QC J1H 4C4, Canada"},{"name":"D\u00e9partement de G\u00e9nie \u00c9lectrique et de G\u00e9nie Informatique, Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jung, S., Michaud, M., Oudre, L., Dorveaux, E., Gorintin, L., Vayatis, N., and Ricard, D. 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