表面肌電訊號（sEMG）被廣泛使用於各種應用，管理有電池容量限制的sEMG無線身體感測網路（WBSN）的功率消耗是一個重要的主題，本文以疲勞評估研究為例，並運用分散式計算概念來探討運算量分配對於系統的影響，我們提出了一種基於乒乓緩衝機制的省電方法，並評估了所有影響功率消耗的關鍵因素，綜上所述，我們對所有可能的分散式計算架構進行全面分析以決定最低功率消耗的WBSN架構。經實驗結果表明，與先前的sEMG系統相比，所提出的WBSN架構的平均電流降低了81.7％。並且在配備相同300mAh 鋰電池於連續無線連接下，電池續航力是先前 sEMG 系統的4.48倍。與商用設備相比，電池續航力是商用設備的1.6倍。

Surface ElectroMyoGraphy (sEMG) is widely applied to a variety of applications. Managing the power consumption of battery-constrained sEMG Wireless Body Sensor Networks (WBSN) is an important topic. In this thesis, we use fatigue assessment as a case study and apply the concept of distributed computing to explore the impact of computation allocations on the WBSN system. We propose a power-saving method based on the ping-pong buffer mechanism and evaluate all the crucial factors which affect power consumption. To sum up, we conduct a comprehensive analysis of all possible distributed computing architectures to determine the lowest-power WBSN architecture. Experimental results showed that the average current of the proposed architecture can be reduced by 81.7% compared with the previous work. Besides, the battery life is 4.48 times that of the previous work under the continuous wireless connection equipped with the same 300mAh lithium battery. Compared with the commercial device, the battery life is 1.6 times that of the commercial device.

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