{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T14:49:27Z","timestamp":1777301367829,"version":"3.51.4"},"reference-count":52,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T00:00:00Z","timestamp":1614816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Development of Customized Contents Provision Technology for Realistic Disaster Management Based on Spatial Information Program funded by Ministry of the Interior and Safety of Korean government.","award":["20DRMS-B146826-03"],"award-info":[{"award-number":["20DRMS-B146826-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sarcopenia can cause various senile diseases and is a major factor associated with the quality of life in old age. To diagnose, assess, and monitor muscle loss in daily life, 10 sarcopenia and 10 normal subjects were selected using lean mass index and grip strength, and their gait signals obtained from inertial sensor-based gait devices were analyzed. Given that the inertial sensor can measure the acceleration and angular velocity, it is highly useful in the kinematic analysis of walking. This study detected spatial-temporal parameters used in clinical practice and descriptive statistical parameters for all seven gait phases for detailed analyses. To increase the accuracy of sarcopenia identification, we used Shapley Additive explanations to select important parameters that facilitated high classification accuracy. Support vector machines (SVM), random forest, and multilayer perceptron are classification methods that require traditional feature extraction, whereas deep learning methods use raw data as input to identify sarcopenia. As a result, the input that used the descriptive statistical parameters for the seven gait phases obtained higher accuracy. The knowledge-based gait parameter detection was more accurate in identifying sarcopenia than automatic feature selection using deep learning. The highest accuracy of 95% was achieved using an SVM model with 20 descriptive statistical parameters. Our results indicate that sarcopenia can be monitored with a wearable device in daily life.<\/jats:p>","DOI":"10.3390\/s21051786","type":"journal-article","created":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T00:39:07Z","timestamp":1614904747000},"page":"1786","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Identification of Patients with Sarcopenia Using Gait Parameters Based on Inertial Sensors"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1822-1752","authenticated-orcid":false,"given":"Jeong-Kyun","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Computer Software, ICT, University of Science and Technology, Daejeon 34113, Korea"},{"name":"Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Myung-Nam","family":"Bae","sequence":"additional","affiliation":[{"name":"Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kang Bok","family":"Lee","sequence":"additional","affiliation":[{"name":"Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8135-3286","authenticated-orcid":false,"given":"Sang Gi","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Computer Software, ICT, University of Science and Technology, Daejeon 34113, Korea"},{"name":"Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1093\/ageing\/afq034","article-title":"Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People","volume":"39","author":"Baeyens","year":"2010","journal-title":"Age Ageing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1016\/j.clnu.2016.02.002","article-title":"Cut-off points to identify sarcopenia according to European Working Group on Sarcopenia in Older People (EWGSOP) definition","volume":"35","author":"Bahat","year":"2016","journal-title":"Clin. 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