Managing the power consumption of b

Managing the power consumption of battery-constrained sEMG Wireless Body Sensor Networks (WBSN) is an important topic. We use fatigue assessment as an application.This paper proposes a distributed computing architecture. We propose a power saving method based on ping-pong buffer mechanism and evaluate all the crucial factors which affect the power consumption such as sEMG sample rate, numbers of sensors, algorithmic computational cost, CPU clock rate, wireless throughput, and selection of wireless technology. After evaluating the factors, we implemented the system. The average current of proposed architecture can be reduced by 83.7\% compared with the previous work. Besides, the battery life is six times that of the previous work under the continuous wireless connection equipped with the same 300mAh lithium battery. Compared with the commercial device, our proposed system reduces the power consumption by 13.7\%, and the battery life is 1.75 times that of the commercial device. In the future, the proposed power-management strategies can be templates to apply on other sEMG applications to design a low-power WBSN architecture.