{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T23:37:43Z","timestamp":1793144263219,"version":"4.5.0"},"reference-count":40,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2022R1C1C1004134"],"award-info":[{"award-number":["2022R1C1C1004134"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J cachexia sarcopenia muscle"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The relationship between physical function, musculoskeletal disorders and sarcopenia is intricate. Current physical function tests, such as the gait speed test and the chair stand test, have limitations in eliminating subjective influences. To overcome this, smart devices utilizing inertial measurement unit sensors and artificial intelligence (AI)\u2010based methods are being developed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We employed cutting\u2010edge technologies, including the smart insole device and pose estimation based on AI, along with three classification models: random forest (RF), support vector machine and artificial neural network, to classify control and sarcopenia groups. Patient data of 83 individuals were divided into train and test sets, with approximately 67% allocated for training. Classification models were implemented using RStudio, considering individual and combined variables obtained through pose estimation and smart insole measurements.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Performance evaluation of the classification models utilized accuracy, precision, recall and F1\u2010score indicators. Using only pose estimation variables, accuracy ranged from 0.92 to 0.96, with F1\u2010scores of 0.94\u20130.97. Key variables identified by the RF model were \u2018Hip_dif\u2019, \u2018Ankle_dif\u2019 and \u2018Hipankle_dif\u2019. Combining variables from both methods increased accuracy to 0.80\u20131.00, with F1\u2010scores of 0.73\u20131.00.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>In our study, a classification model that integrates smart insole and pose estimation technology was assessed. The RF model showed impressive results, particularly in the case of the Hip and Ankle variables. The growth of advanced measurement technologies suggests a promising avenue for identifying and utilizing additional digital biomarkers in the management of various disorders. The convergence of AI technologies with diagnostics and treatment approaches a promising future for enhanced interventions in conditions like sarcopenia.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jcsm.13356","type":"journal-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T03:03:24Z","timestamp":1698375804000},"page":"2793-2803","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Sarcopenia classification model for musculoskeletal patients using smart insole and artificial intelligence gait analysis"],"prefix":"10.1002","volume":"14","author":[{"given":"Shinjune","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Biomedical Research Institute Inha University Hospital  Incheon South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyeon Su","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Research Institute Inha 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