{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T15:34:24Z","timestamp":1767108864409,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T00:00:00Z","timestamp":1681430400000},"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>This paper presents a novel approach to creating a graphical summary of a subject\u2019s activity during a protocol in a Semi Free-Living Environment. Thanks to this new visualization, human behavior, in particular locomotion, can now be condensed into an easy-to-read and user-friendly output. As time series collected while monitoring patients in Semi Free-Living Environments are often long and complex, our contribution relies on an innovative pipeline of signal processing methods and machine learning algorithms. Once learned, the graphical representation is able to sum up all activities present in the data and can quickly be applied to newly acquired time series. In a nutshell, raw data from inertial measurement units are first segmented into homogeneous regimes with an adaptive change-point detection procedure, then each segment is automatically labeled. Then, features are extracted from each regime, and lastly, a score is computed using these features. The final visual summary is constructed from the scores of the activities and their comparisons to healthy models. This graphical output is a detailed, adaptive, and structured visualization that helps better understand the salient events in a complex gait protocol.<\/jats:p>","DOI":"10.3390\/s23084000","type":"journal-article","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T10:29:16Z","timestamp":1681468156000},"page":"4000","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6580-5027","authenticated-orcid":false,"given":"Sylvain","family":"Jung","sequence":"first","affiliation":[{"name":"Universit\u00e9 Paris Saclay, Universit\u00e9 Paris Cit\u00e9, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France"},{"name":"Universit\u00e9 Sorbonne Paris Nord, L2TI, UR 3043, F-93430 Villetaneuse, France"},{"name":"AbilyCare, 130 Rue de Lourmel, F-75015 Paris, France"},{"name":"ENGIE Lab CRIGEN, F-93249 Stains, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6333-7311","authenticated-orcid":false,"given":"Nicolas","family":"de l\u2019Escalopier","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris Cit\u00e9, Universit\u00e9 Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006 Paris, France"},{"name":"Service de Neurologie, Service de Sant\u00e9 des Arm\u00e9es, HIA Percy, F-92190 Clamart, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4750-2265","authenticated-orcid":false,"given":"Laurent","family":"Oudre","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris Saclay, Universit\u00e9 Paris Cit\u00e9, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France"}]},{"given":"Charles","family":"Truong","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris Saclay, Universit\u00e9 Paris Cit\u00e9, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1606-6461","authenticated-orcid":false,"given":"Eric","family":"Dorveaux","sequence":"additional","affiliation":[{"name":"AbilyCare, 130 Rue de Lourmel, F-75015 Paris, France"}]},{"given":"Louis","family":"Gorintin","sequence":"additional","affiliation":[{"name":"Novakamp, 10-12 Avenue du Bosquet, F-95560 Baillet en France, France"}]},{"given":"Damien","family":"Ricard","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris Cit\u00e9, Universit\u00e9 Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006 Paris, France"},{"name":"Service de Neurologie, Service de Sant\u00e9 des Arm\u00e9es, HIA Percy, F-92190 Clamart, France"},{"name":"Ecole du Val-de-Gr\u00e2ce, Service de Sant\u00e9 des Arm\u00e9es, F-75005 Paris, France"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.1007\/s00415-017-8424-0","article-title":"Freezing of gait and fall detection in Parkinson\u2019s disease using wearable sensors: A systematic review","volume":"264","author":"Evers","year":"2017","journal-title":"J. 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