Towards Deep Learning-Based Sarcopenia Screening with Body Joint Composition Analysis

Sarcopenia, a newly recognized geriatric syndrome, now prevalent in the rapidly aging region of Asia, is characterized by the age-related decline of skeletal muscle mass plus relatively low muscle strength and/or physical performance. Doctors screen for sarcopenia by observing patients’ habitual gait features without quantification and the performance of gait disturbances differ in various people that are considered to be sarcopenic, which is an important basis along with reduced physical functioning for the diagnosis of sarcopenia. Such a subjective diagnosis has been seen as a problem because diagnostic results may differ among doctors and factors such as fatigue may affect diagnosis. To strengthen and aid the use of these observations, we built a novel automatic deep learning model based on random forest for real-time human body joint detection coupled with a modified Long Short-Term Memory (LSTM) to recognize gait features for further clinical analysis. Aligned with the Asian Working Group for Sarcopenia (AWGS) [1] aims, our goal is to facilitate the implementation of standardized sarcopenia diagnosis in clinical practice by providing an automatic gait analysis system. Our model is recorded from geriatric patients for whole gait understanding. Experimental results demonstrate that our proposed model improves gait recognition performance compared to baseline methods. We believe, the quantitative evaluation provided by our method will assist the clinical diagnosis of sarcopenia and the experimental results on our gait datasets verify the feasibility and effectiveness of the proposed method.