Rehabilitation

Operating system

Psychiatry

Machine learning

Physical therapy

Artificial intelligence

Wavelet transform

Normality

Computer science

Process (computing)

Wavelet

Medicine

Physical medicine and rehabilitation

ACM Transactions on Sensor Networks

In order to better assist the rehabilitation treatment of patients with musculoskeletal injury, standard rehabilitation actions are needed to guide the musculoskeletal rehabilitation process. With more and more urgent demands, the musculoskeletal rehabilitation evaluation systems have attracted a high degree of attention. Experts have proposed a series of systems based on laser, ultrasound, and image, which can give reasonable recognition and judgment. However, these systems either require specialized and expensive equipment or can be affected by ionizing radiation. How to construct a musculoskeletal rehabilitation evaluation system with low cost, good effect, and little injury is still a great challenge. In this article, we propose MSEva, a musculoskeletal rehabilitation evaluation system based on EMG signals. Specifically, the system uses EMG sensors to collect a large amount of data for five rehabilitation actions. Secondly, MSEva uses Wavelet Transform (WT) to extract the signal features and then puts the processed data into the Long Short-Term Memory (LSTM) network for model training. Finally, the system uses the LSTM model to evaluate the normality of the EMG response of rehabilitation actions. The results show that the average accuracy of MSEva reaches 94.37%, which has important evaluation value in guiding the rehabilitation of musculoskeletal patients.

MSEva: A Musculoskeletal Rehabilitation Evaluation System Based on EMG Signals