Abstract
Surface electromyography (sEMG) signals are closely related to the activation of human muscles and the motion of the human body, which can be used to estimate the dynamics of human limbs in the rehabilitation field. They also have the potential to be used in the application of bilateral rehabilitation, where hemiplegic patients can train their affected limbs following the motion of unaffected limbs via some rehabilitation devices. Traditional methods to process the sEMG focused on motion pattern recognition, namely, discrete patterns, which are not satisfactory for use in bilateral rehabilitation. In order to overcome this problem, in this paper, we built a relationship between sEMG signals and human motion in elbow flexion and extension on the sagittal plane. During the conducted experiments, four participants were required to perform elbow flexion and extension on the sagittal plane smoothly with only an inertia sensor in their hands, where forearm dynamics were not considered. In these circumstances, sEMG signals were weak compared to those with heavy loads or high acceleration. The contrastive experimental results show that continuous motion can also be obtained within an acceptable precision range.
Highlights
Stroke is one of the most harmful diseases which results in adults losing their ability to read, speak, understand and move
We proposed a method to detect the motion of unaffected limbs without a constraint, which is to adapt surface electromyography signals [5]
We focused on the recognition of human elbow flexion and extension on the sagittal plane for bilateral rehabilitation with only one electrode attached to the bicep muscle
Summary
Stroke is one of the most harmful diseases which results in adults losing their ability to read, speak, understand and move. Based on reports from seven countries (the US, France, Germany, Italy, Spain, the UK and Japan), stroke occurs in an average of 214 out of 100,000 people per year, with the incidence rate growing annually by 1.9% due to an aging population [1]. With the increase of the aging population, new rehabilitation technology is required more and more urgently, especially using robotics in home rehabilitation. Compared to traditional rehabilitation strategies, robot-mediated rehabilitation has been widely considered as a promising method to improve patient access to therapy [2]. There are several rehabilitation strategies used widely in robot-mediated rehabilitation, including passive training, active training and active-resistce training
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