Design of Fatigue Grade Classification System Based on Human Lower Limb Surface EMG Signal

Abstract

With the deepening of the aging of China’s population, more and more people suffer from stroke. Stroke has three characteristics: high morbidity, high mortality, and high disability rate. At present, stroke has become one of the main causes of human death, and the population suffering from a stroke in China is gradually becoming younger, many patients can not work and live normally, destroying many happy families. However, stroke is not invincible. Once suffering from stroke, patients can still live and work independently as long as they actively carry out rehabilitation training. The surface EMG signal contains abundant physiological information and has remarkable effects on nerve rehabilitation and orthopedic rehabilitation. Patients with rehabilitation training less training can not play a rehabilitation effect, and excessive training is easy cause secondary injuries, therefore, this paper will design a fatigue state classification system based on surface EMG signals of human lower limb muscles, and analyze the fatigue state of patients’ lower limbs by collecting surface EMG signals of target muscles of human lower limbs, to ensure that patients can not only carry out effective training but also not cause secondary injuries due to excessive training.