An Intelligent Method for Upper Limb Posture Recognition Based on Limited MEMS Data

There are more than 10 million new stroke cases worldwide every year, and stroke has become one of the main causes of death and disability. In recent years, with the rapid development of computer science and technology, through the combination of Internet of things, deep learning, big data and other emerging technologies with traditional medicine, a new field of intelligent medicine has been developed. The scene of this paper is for stroke patients to use functional electrical stimulation equipment for rehabilitation training. By preprocessing the collected training data of MEMS patients, combined with the fully connected neural network (FCNN) model, the patient's upper limb posture can be intelligently recognized, which can make the intervention control of the rehabilitation system more efficient and intelligent. However, due to the damage of the stroke patients' action function, the existing sample data scale is small. In order to solve the problem of over fitting of network model caused by limited sample data in intelligent posture recognition, This paper proposes to expand the sample through data windowing operation to obtain a better performance recognition model.