Abstract
The surface electromyography (sEMG) signal is widely used as a control source of the upper limb exoskeleton rehabilitation robot. However, the traditional way of controlling the exoskeleton robot by the sEMG signal requires one to specially extract and calculate for complex sEMG features. Moreover, due to the huge amount of calculation and individualized difference, the real-time control of the exoskeleton robot cannot be realized. Therefore, this paper proposes a novel method using an improved detection algorithm to recognize limb joint motion and detect joint angle based on sEMG images, aiming to obtain a high-security and fast-processing action recognition strategy. In this paper, MobileNetV2 combined the Ghost module as the feature extraction network to obtain the pretraining model. Then, the target detection network Yolo-V4 was used to estimate the six movement categories of the upper limb joints and to predict the joint movement angles. The experimental results showed that the proposed motion recognition methods were available. Every 100 pictures can be accurately identified in approximately 78 pictures, and the processing speed of every single picture on the PC side was 17.97 ms. For the train data, the mAP@0.5 could reach 82.3%, and mAP@0.5–0.95 could reach 0.42; for the verification data, the average recognition accuracy could reach 80.7%.
Highlights
Upper limb movement is very central for daily activities; there are about15 million people a year who suffer from stroke worldwide, with 5 million stroke survivors who experience permanent motor disability and require therapeutic services [1]
This paper studies the surface electromyography (sEMG) signals of six types of motions with three similar; the amplitudes were opposite to each other
The correlation of sEMG signals among the three degrees of freedom was analyzed; each degree of freedom of the marked data was set into a class to identify the correlation of degrees of freedom
Summary
Upper limb movement is very central for daily activities; there are about. 15 million people a year who suffer from stroke worldwide, with 5 million stroke survivors who experience permanent motor disability and require therapeutic services [1]. Intervention in rehabilitation therapies has a desirable effect for the recovery of patients [2]. The robot exoskeleton can assist hemiplegia patients with pretraining their own rehabilitation exercise, guide the correct track to perform the movement and give the support of the auxiliary force directly. To improve the efficiency of the whole rehabilitation process, there are many technical difficulties in the field of exoskeleton robotics. How to make the exoskeletons work as flexibly as a real limb is a key point [3,4,5].
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call