A Compact Home-Based Training System for Preventing Frailty Using a Mapping Model and Cross-Dataset Transfer Learning

Abstract

Frailty is becoming a more serious issue as the population ages. Numerous studies have shown that exercise can effectively slow the development of frailty. Compared with vigorous exercise, Baduanjin (BDJ), a kind of traditional Chinese Qigong with eight simple movements, is more suitable for frailty patients. BDJ has been used to train frailty patients by physical therapists. To provide an enhanced training method, we designed a lightweight family-based frailty training system via a virtual BDJ coach. To achieve a compact system, we use a webcam as the main device. The system also supports the Kinect framework. We use pose estimation and motion recognition methods to analyze the user's movements. In addition, a novel transfer learning method is proposed. We designed a mapping model called “Skeleton Mapnet” to convert skeletal data from different frameworks. This method enables datasets from different frameworks to share classification models. It can also mix skeletal data from different frameworks to solve the lack of webcam datasets. Such a design allows the system to be easily ported into other platforms. In addition, the system is also suitable for the use of the artificial intelligence of things. Our design ensures that frailty patients can easily learn and operate the system.