Non-invasive tools for early detection and monitoring of sarcopenia in older individuals

Abstract

Sarcopenia, defined by a gradual loss of muscle mass and function, affects many elderly people and is associated with reduced mobility, greater frailty, and a higher risk of falling. Using a vision attentive model and integrating the embedded Timed Up and Go Test (TUG-T), 3-meter Walk Test (3mW-T), and fall risk analysis, this research proposes a unique method for assessing sarcopenia in elderly people. The attentive vision model uses computer vision techniques to examine TUG activities and gait speed in real-time, offering insightful information about the elderly's the functional ability and muscular strength. Moreover, this approach provides a more comprehensive assessment of sarcopenia by integrating falling risk analysis. The proposed system achieved an overall accuracy of 86.6%, outperforming the individual components: TUG test (84.0%, p<0.05), gait speed (88.2%, p<0.05), and fallen risk assessment (93.0%, p<0.05). The results indicate that this novel strategy has enormous potential for aged healthcare, enabling targeted therapies and enhancing the overall quality of life for older people at risk of issues connected to sarcopenia.