Wearable EMG Bridge-A Multiple-Gesture Reconstruction System Using Electrical Stimulation Controlled by the Volitional Surface Electromyogram of a Healthy Forearm

• Published in: IEEE access 2020, Vol.8, p.137330-137341
• Main Authors: Bi, Zhengyang, Wang, Yunlong, Wang, Haipeng, Zhou, Yuxuan, Xie, Chenxi, Zhu, Lisen, Wang, Hongxing, Wang, Bilei, Huang, Jia, Lu, Xiaoying, Wang, Zhigong
• Format: Article
• Language: English
• Subjects: Detectors; Electric bridges; Electrical stimulation; Electromyography; Forearm; Iron; linear discriminant analysis (LDA); multiple-gesture reconstruction; Muscles; paralysis; Prototypes; surface electromyography (sEMG); Wrist
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3011710

In this study, a wearable prototype system was developed for multiple-gesture rehabilitation using electrical stimulation controlled by a volitional surface electromyography (sEMG) scan of a healthy forearm. The purpose of the prototype system is to reconstruct multiple gestures of a paralysed limb and to simplify the positioning of sEMG detection sites on a healthy forearm. A self-designed eight-channel sEMG detection armband was used to detect the sEMG signal distributions of the muscle groups in healthy forearms. Linear discriminant analysis (LDA) was used to classify the sEMG signal distributions corresponding to different gestures, and then the classification results were mapped to corresponding stimulation channels. The sEMG signal with the maximum root mean square (RMS) was used as the source of stimulus coding for each gesture. Our proposed mean absolute value (MAV)/number of slope sign changes (NSS) dual-coding (MNDC) algorithm was used to encode the sEMG signal into an electrical stimulus with a dynamic pulse width and frequency. The constant-current stimulation armband electrically stimulated multiple muscles in the affected forearm by means of a circuit designed with a time-division multiplexed stimulation channel. An experiment involving 6 able-bodied volunteers showed that when the detection armband was located near the middle of the forearm, the gesture classification accuracy was greater than 90%, and each active sEMG signal was high. Gesture bridge experiments, including grasping, wrist flexion, wrist extension and finger extension, were carried out among six hemiplegic subjects and between one able-bodied volunteer acting as a controller and each of six stroke patients as the controllee. Both sets of results show that the proposed system can reconstruct these four gestures in the controlled subject with a delay of at most 360 ms and with a correlation coefficient of >0.72.