With the aging of the population,the increase in the number of disabled people,and the increase in patients with chronic diseases,the demand for rehabilitation medical services in China continues to rise,but the awareness of rehabilitation is weak,the development of rehabilitation medicine education lags behind,the lack of rehabilitation professionals,and there's shortage of rehabilitation resources and uneven distribution,all of which has made the contradiction between supply and demand for rehabilitation treatment extremely prominent.At present,traditional rehabilitation training methods in which rehabilitation therapists conduct one-on-one training cannot meet the treatment needs at this stage.In recent years,with the development of artificial intelligence technology,artificial intelligence technology has become more and more widely used in the field of rehabilitation.In particular,rehabilitation robots,as the main tool,are expected to solve the current contradiction between supply and demand in rehabilitation medical care and improve the development of China's rehabilitation medical system.At present,the development of rehabilitation robots is still in its infancy,with huge development potential,and also faces many difficulties and challenges.Artificial intelligence includes machine learning.Among them,deep learning is an important branch of machine learning.It is widely used in the recognition and classification of motor intentions of surface electromyography and electroencephalography.It can enable rehabilitation robots to assist patients in completing corresponding motor training,with a good prospect for application.This paper reviews the research progress of deep learning applied to motor intention recognition using surface electromyography and electroencephalography,in order to provide reference for experts and scholars in related fields in the research of rehabilitation robots.
万方数据
维普
