Abstract
The global increase in the older adult population highlights the need for effective frailty assessment, a condition linked to adverse health outcomes such as hospitalization and mortality. Existing frailty assessment tools, like the Fried phenotype and Rockwood score, have practical limitations, necessitating a more efficient approach. This study aims to enhance frailty prediction accuracy in older adults using a combined biomechanical and deep learning approach. We recruited 312 participants (126 non-frail, 145 pre-frail, 41 frail) and assessed frailty using the Fried index, upper-extremity function (UEF) test, and muscle force calculations. Machine learning (ML) models, including logistic regression and support vector machine (SVM), were employed alongside deep learning with long short-term memory (LSTM) networks. Results showed that incorporating muscle model parameters significantly improved frailty prediction. The LSTM model achieved the highest accuracy (74%), outperforming SVM (67%) and regression (66%), with precision and F1 scores of 81% and 75%, respectively. Notably, muscle co-contraction emerged as a critical predictor, with frail individuals exhibiting substantially higher levels. Our findings demonstrate that integrating UEF tasks with deep learning models provides superior frailty prediction, potentially offering a robust, efficient clinical tool. However, further validation with larger, more diverse populations is needed to confirm the generalizability of our results. This study underscores the potential of advanced computational techniques to improve the identification and management of frailty in older adults.