Jamsrandorj, Ankhzaya Jung, Heeeun Lee, Daehyun Kim, Jinwook Mun, Kyung-Ryoul 2026-04-08T09:30:24Z 2026-04-08T09:30:24Z 2026-03-27 2025-07 2375-7477 https://pubs.kist.re.kr/handle/201004/154552 As the global population ages and life expectancy increases, early detection and continuous monitoring of sarcopenia-an age-related decline in muscle mass and strength-are critical for promoting healthy aging. Traditional assessment methods rely on expensive, specialized medical equipment and expert intervention, limiting their practicality for everyday use. To address these challenges, this study proposes a novel vision-based approach for identifying sarcopenia using gait. A total of 92 elderly individuals participated, including 60 patients with sarcopenia and 32 healthy controls. Digital cameras captured each participant's walking motion, from which 2D skeleton sequences were extracted. Our deep learning model, trained on these 2D skeleton sequences along with additional gait-related features, classified sarcopenia and healthy controls with 82.88% sample-wise accuracy and 94.44% subject-wise accuracy on the test dataset. English IEEE Deep Learning-Based Sarcopenia Classification through Gait Video Analysis with a Single Mobile Camera Conference 10.1109/EMBC58623.2025.11254612 1 47th International Conference of the Engineering in Medicine and Biology Society-EMBC-Annual 47th International Conference of the Engineering in Medicine and Biology Society-EMBC-Annual US Copenhagen, DENMARK 2025-07-14 2025 47TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) 001683462200694 2-s2.0-105023799503