The application of neural network approaches for physical rehabilitation

Abstract

Physical rehabilitation is essential for a large number of patients around the world to recover from their disabilities. However, conventional methods of rehabilitation were expensive and difficult for majority of patients to access. This paper presents studies with neural network-based approaches that solve this problem are reviewed in this paper. The methods are divided into three main categories: stroke rehabilitation, injury rehabilitation and other rehabilitations. The common methods reviewed are Convolutional Neural Network (CNN), Support Vector Machine (SVM) and Recurrent Neural Network (RNN). There are various rehabilitation datasets reviewed in the paper, which all included pictures and videos of both patients and healthy people preforming a series of movements. The sensors used in experiments to capture patients movements are also summarized in this paper. The paper reviewed few algorithms able to model a 3D human skeleton based on the data collected by sensors. Evaluation metrics reviewed includes Discrete Movement Score, rule-based and template-based scoring methods. K-Nearest Neighbor (KNN) and Dynamic Time Warping (DTW) distance function were commonly used in template-based evaluation methods. The results in researches reviewed indicate that neural network-based rehabilitation methods are able to satisfy most demands, and improved efficiency, making it affordable and accessible to more patients.