지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.7
- 수록면
- 1 - 8 (8page)
- DOI
- 10.12786/bn.2024.17.e10
이용수
초록· 키워드
오류제보하기
Sarcopenia, a condition characterized by muscle weakness and mass loss, poses significant risks of accidents and complications. Traditional diagnostic methods often rely on physical function measurements like handgrip strength which can be challenging for affected patients, including those with stroke. To address these challenges, we propose a novel sarcopenia diagnosis model utilizing stimulated muscle contraction signals captured via wearable devices. Our approach achieved impressive results, with an accuracy of 93% and 100% in sarcopenia classification for male and female stroke patients, respectively. These findings underscore the significance of our method in diagnosing sarcopenia among stroke patients, offering a non-invasive and accessible solution.
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