TY - GEN
T1 - An Experimental Study of Digital Communication System with Human Body as Communication Channel
AU - Zhang, Chengyi
AU - Jin, Qingyun
AU - Zhao, Mohan
AU - Zhang, Dingguo
AU - Lin, Lin
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported in part by the National Natural Science Foundation, China, under Grant 61971314, in part by the Science and Technology Commission of Shanghai Municipality under Grant 19510744900, and in part by the Sino-German Center of Intelligent Systems, Tongji University.
PY - 2022/9/30
Y1 - 2022/9/30
N2 - For a long time, people have carried out various studies on human body communication (HBC) in order to establish a suitable communication link through human body. However, in the galvanic coupled method of HBC, the high current intensity is rarely used to implement the communication link. In the medical field, functional electrical stimulation (FES) is often used to send high intensity electrical pulses to make muscles contract, and this contraction phenomenon will generate surface electromyography (sEMG) signals on the surface of human skins. According to this principle and the galvanic coupling method of HBC, we propose a new digital communication system based on FES and sEMG signal detection with human body as communication channel in this paper. We modulate the transmitted signal into electrical stimulation to stimulate the muscles and detect the sEMG signal caused by it to achieve a complete communication process. The framework of the entire communication system is proposed. Its error performance for different stimulation parameters is tested and evaluated by experiments. Using FES and sEMG signal detection, our work makes a new exploration of HBC at high current intensities and enables a complete communication link. This work is expected to be applied to the HBC design combined with electrical stimulation in medical field.
AB - For a long time, people have carried out various studies on human body communication (HBC) in order to establish a suitable communication link through human body. However, in the galvanic coupled method of HBC, the high current intensity is rarely used to implement the communication link. In the medical field, functional electrical stimulation (FES) is often used to send high intensity electrical pulses to make muscles contract, and this contraction phenomenon will generate surface electromyography (sEMG) signals on the surface of human skins. According to this principle and the galvanic coupling method of HBC, we propose a new digital communication system based on FES and sEMG signal detection with human body as communication channel in this paper. We modulate the transmitted signal into electrical stimulation to stimulate the muscles and detect the sEMG signal caused by it to achieve a complete communication process. The framework of the entire communication system is proposed. Its error performance for different stimulation parameters is tested and evaluated by experiments. Using FES and sEMG signal detection, our work makes a new exploration of HBC at high current intensities and enables a complete communication link. This work is expected to be applied to the HBC design combined with electrical stimulation in medical field.
KW - functional electrical stimulation (FES)
KW - Human body communication (HBC)
KW - surface electromyography (sEMG)
UR - http://www.scopus.com/inward/record.url?scp=85142227842&partnerID=8YFLogxK
U2 - 10.1109/BSN56160.2022.9928477
DO - 10.1109/BSN56160.2022.9928477
M3 - Chapter in a published conference proceeding
AN - SCOPUS:85142227842
T3 - BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings
BT - BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings
PB - IEEE
CY - U. S. A.
T2 - 2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, BSN 2022
Y2 - 27 September 2022 through 30 September 2022
ER -