中風病患的復健過程中，測量肌電訊號不僅能評估患者肌耐力回復程度的指標，而且能藉由多通道肌電訊號的量測觀察不同肌肉間的出力模式評估出病人的協調性。本論文的研究目的即是利用低耗能小體積的無線傳輸模組發展一套輕便的多通道無線肌電訊號量測系統，以便病患量測。在研究一開始先利用NI公司生產的NI9205與WLS9163達到肌電訊號的數位化與資料傳輸進而完成一套初步的肌電量測系統，然而類比肌電訊號在經過長距離導線傳輸到NI9205轉成數位資料之間受到許多雜訊干擾影響訊號品質，因此本研究接著利用Xbee傳輸模組與SIOC開發板結合開發一套可多通道量測的無線肌電量測系統，並且利用Labview設計一套觀測與分析介面，將量測完成儲存下來的肌電訊號分析出有用的數據，並且可在之後利用Labview進行介面的調整與需求功能的追加。為了測試感測器的訊號品質，我們對四位無疾病正常的受試者(3男1女, 年齡23-25)進行同一個案的不同量測與不同個案間量測的差異性測試。本研究使用了Shimmer公司的無線肌電量測系統當成參照，與本研究利用Zigbee傳輸協定完成的測量系統和利用NI公司的無線傳輸產品完成的測量系統三者比較，將各個系統單顆感測器測得的肌電訊號進行比較分析，同時也進行了肌肉疲勞指數的測量，結果顯示同一個案的訊號品質比較中，SNR平均值較高的系統其標準差較大，其因為高訊雜比的量測系統其SNR值對於肌電訊號的波動較為敏感；而在不同個案間的量測系統訊號品質比較中，Zigbee的訊號品質與Shimmer訊號品質在個案間平均SNR值表現為22.7 1.38 dB與23.6 1.98 dB及變動量分別為6% 與 8.3%，表示訊號品質相近，疲勞指數的量測也均顯示出頻率指數可明顯的觀察到疲勞現象；在多通道測量之前，先行測試多顆感測器訊號品質是否一致，結果顯示感測器的訊號品質SNR均在24dB左右，接著進行的多通道肌電訊號量測，同樣為四位無疾病正常的受試者進行測量，並藉由比較肌肉共同收縮係數(Co-contraction index, CI)整理出不同動作肌肉間的出力關係，在最後探討三位病患的肌電訊號中，觀察出患者肌電數據異常處，並且可看出復健前與復健後明顯的肌肉出力模式回歸正常與肌力增強的現象，總結以上結果可看出藉由肌電訊號的量測，不僅可利用Vpp與RMS得知正常人的出力情形、計算median frequeny得知肌肉疲勞程度，也能藉由CI參數的運算得知肌群的出力模式，進而利用這些參數成為病患復健過程中肌力與動作協調性的參考指標。

In the course of stroke patient rehabilitation, electromyography (EMG) provides not only an indicator of muscle strength and endurance but also an observation on co-ordination of muscles. The purpose of this study is to develop a small-sized wireless EMG measurement system for patient’s convenience. In this study, commercial available data acquisition (NI9205) and wireless transmission (WLS9163) devices were first used for acquiring and transmitting EMG signal to test the function of the signal conditioning circuits. However, the noise interference induced by the long lead wire between the electrode and NI9205 contaminated the EMG signal. This study used Zigbee module and embedded system board to develop a better wireless electromyography measurement system with a Labview-based software tool to monitor, display and analyze the signal. The function of the software tool could be expanded with Labview. In order to verify the signal quality of sensor, intra- and inter-subject variability of EMG signals were measured on four healthy subjects (3 males and 1 female, age 23-25) with three different wireless EMG measurement systems ( i.e., Shimmer research Inc., our Zigbee-based and NI-based measurement systems) in terms of signal-to-noise ratio (SNR) and spectral fatigue index. The results show that both Zigbee-based system (22.7 1.38 dB) and Shimmer system (23.6 1.98 dB) were with similar inter-subject SNRs and variation (6% vs. 8.3%). Spectral fatigue indices of both systems also indicated obvious appearance of muscle fatigue. Before multi-channel EMG measurement, whether the signal quality of sensors is identical has been examined. The results show that sensors’ signal SNRs are all about 24 1.5dB. We then conducted the multi-channel EMG measurement on the same four healthy subjects with three different movements (i.e., shoulder flexion, abduction, and extention). By calculating Co-contraction index (CI), characteristics in different movements were identified. Finally, the EMG signals of three stroke patients were measured and analyzed. Based on the CI calculation, the EMG signals before and after rehabilitation could show muscular recovery from abnormal waveform before rehabilitation and muscular power enforced after rehabilitation. These results imply that EMG signals could be used as an index of efficacy for the rehabilitation.

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