{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:22:48Z","timestamp":1760145768956,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Quebec Parkinson Network (QPN), Parkinson Quebec, and Canada Research Chair in Biomechanical Movement Signature","award":["#CRC-2019-00214"],"award-info":[{"award-number":["#CRC-2019-00214"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background: The automatic detection of activities of daily living (ADL) is necessary to improve long-term home-based monitoring of Parkinson\u2019s disease (PD) symptoms. While most body-worn sensor algorithms for ADL detection were developed using laboratory research systems covering full-body kinematics, it is now crucial to achieve ADL detection using a single body-worn sensor that remains commercially available and affordable for ecological use. Aim: to detect and segment Walking, Turning, Sitting-down, and Standing-up activities of patients with PD using a Smartwatch positioned at the ankle. Method: Twenty-two patients living with PD performed a Timed Up and Go (TUG) task three times before engaging in cleaning ADL in a simulated free-living environment during a 3 min trial. Accelerations and angular velocities of the right or left ankle were recorded in three dimensions using a Smartwatch. The TUG task was used to develop detection algorithms for Walking, Turning, Sitting-down, and Standing-up, while the 3 min trial in the free-living environment was used to test and validate these algorithms. Sensitivity, specificity, and F-scores were calculated based on a manual segmentation of ADL. Results: Sensitivity, specificity, and F-scores were 96.5%, 94.7%, and 96.0% for Walking; 90.0%, 93.6%, and 91.7% for Turning; 57.5%, 70.5%, and 52.3% for Sitting-down; and 57.5%, 72.9%, and 54.1% for Standing-up. The median of time difference between the manual and automatic segmentation was 1.31 s for Walking, 0.71 s for Turning, 2.75 s for Sitting-down, and 2.35 s for Standing-up. Conclusion: The results of this study demonstrate that segmenting ADL to characterize the mobility of people with PD based on a single Smartwatch can be comparable to manual segmentation while requiring significantly less time. While Walking and Turning were well detected, Sitting-down and Standing-up will require further investigation to develop better algorithms. Nonetheless, these achievements increase the odds of success in implementing wearable technologies for PD monitoring in ecological environments.<\/jats:p>","DOI":"10.3390\/s24175486","type":"journal-article","created":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T03:32:01Z","timestamp":1724643121000},"page":"5486","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Innovative Detection and Segmentation of Mobility Activities in Patients Living with Parkinson\u2019s Disease Using a Single Ankle-Positioned Smartwatch"],"prefix":"10.3390","volume":"24","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"}]},{"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"}]},{"given":"Camille","family":"Martin","sequence":"additional","affiliation":[{"name":"Centre de Recherche sur le Vieillissement, CIUSSS de l\u2019Estrie\u2014CHUS, Sherbrooke, QC J1H 4C4, Canada"}]},{"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"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1111\/jnc.13691","article-title":"The Clinical Symptoms of Parkinson\u2019s Disease","volume":"139","author":"Sveinbjornsdottir","year":"2016","journal-title":"J. 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