| dc.description.abstract | Clinical frailty, also known as frailty syndrome, is a common medical condition among the elderly, characterized by a decline in physical, psychological, and social functioning. It is the result of multiple factors such as age-related physiological changes, chronic diseases, and environmental factors. Clinically frail individuals are more vulnerable to stressors and have a higher risk of falls, hospitalization, disability, and mortality. Early detection of frailty trends can enable proactive interventions and reduce future burdens.
With the advancement of image recognition and skeleton tracking algorithms, several skeleton-based pathological gait classification methods have been proposed in recent years. However, these methods are rarely applied to classify human frailty and cannot replace complex clinical frailty scales. In this paper, by utilizing an LSTM classifier and temporal motion features extracted from full-body skeleton data, we perform a 4-class classification of the four frailty levels in the Clinical Frailty Scale, achieving an f1-score of 73%. By incorporating image recognition to provide background object features related to the environment, the classification accuracy is increased to 93%.
This study demonstrates that the proposed method can support medical and clinical decision-making and meets the requirements of AIoMT, making it easier to generalize across various settings. | en_US |