A comprehensive evaluation method for frailty based on semi-supervised learning and transfer-learning

Frailty evaluation is of great significance for the specific population, which can speed up the treatment process and reduce the adverse effects after treatment. In this article, in order to make up for the shortcomings of the traditional evaluation methods, a comprehensive intelligent evaluation method that integrates physiological data from multiple perspectives, such as mobility, muscle level, and body composition data has been proposed. The multi-source physiological data of 174 participants has been collected, and a series of features related to frailty have been extracted. A recurrent stepping semi-supervised learning (RSSSL) framework is proposed to transmit the knowledge from the annotated data to the unannotated gait sequence utilizing various networks with varying architectures. Moreover, in order to enhance the frailty evaluation's performance, an approach based on subspace transfer learning integrating with heterogeneous features (STL-IHF) is proposed. The mean segmentation accuracy on the unannotated gait sequence reaches over 97.10% across recruited subjects with distinct gait patterns, which is significantly superior when compared with classical deep learning methods. And the recognition accuracy of the STL-IHF method for frailty evaluation reaches over 92.9%. The overall experimental results demonstrate that the proposed methods are effective in frailty evaluation tasks in clinics.