創客精神融入體育之示例—自製 IoT肌電儀探究肌肉疲勞時肌電功率頻譜的特性之個案研究

創客精神融入體育之示例—自製 IoT肌電儀探究肌肉疲勞時肌電功率頻譜的特性之個案研究

運用生理監測的教學科技設備如計步器、心電儀及肌電儀能協助體育教師增進教學效能與達成教學目標。但是大部份的科技設備價格高昂且受限於廠商所提供的軟、硬體限制，難以實現於體育教學現場。依據創客教育的精神，本研究基於Arduino開發環境，發展一低價、可攜式、可連上物聯網（Internet of Things, IoT）平台之單通道肌電儀。藉此自製肌電儀分析業餘鐵人三項運動員之個案，比較其休息三日未接受訓練及完成90公里自行車與5公里路跑訓練後疲勞時之靜態股直肌肌電，經t檢定考驗與使用傅立葉轉換（Fourier transform, FT）後發現有如下特徵：（1）休息時股直肌靜態肌電強度顯著高於疲勞時的肌電強度；（2）休息時的股直肌靜態肌電功率頻譜會於28.689 Hz 附近出現峰值，此頻率附近的範圍可能是肌肉的同步訊號；（3）疲勞時的股直肌靜態肌電功率頻譜會於24.61 Hz、16.219 Hz與7.649 Hz三個頻率附近出現峰值，驗證肌肉疲勞時會出現低頻的肌電現象；（4）肌肉疲勞時，肌電功率頻譜會發生降檔的現象，上述發現可提供給體育教師或運動教練觀察肌肉施力與疲勞現象的客觀參考。文末提出改進此研究的建議。

Physiological monitoring equipment such as pedometers, electrocardiograph machines, and electromyography (EMG) machines can assist physical education teachers in enhancing their teaching effectiveness and achieving their teaching objectives. However, the application of such technological equipment in the physical education venue is limited by high equipment prices and the types of software and hardware provided by manufacturers. According to the concepts of maker education, a low-cost, portable single-channel EMG machine that can connect to an Internet of Things (IoT) platform was created using the Arduino integrated development environment. This self-made EMG machine was used to analyze an amateur triathlete and compare the EMGs of the subject’s rectus femoris during lying flat after resting for three days with no training and after a training session involving biking for 90 km and running for 5 km. T tests and Fourier transform analysis revealed the following: (1) the lying flat of the rectus femoris in a rested state presented significantly higher EMG signal intensity than those in a fatigued state; (2) the EMG power spectrum of lying flat of the rectus femoris in a rested state displayed a peak around 28.689 Hz; the range around this frequency may be the synchronization signals of the muscle; (3) the EMG power spectrum of lying flat of the rectus femoris in a fatigued state presented peaks around 24.61 Hz, 16.219 Hz, and 7.649 Hz, thereby verifying that fatigued muscles show low-frequency EMG; (4) downshifting occurs in the EMG power spectrum of fatigued muscles. These findings can provide physical education teachers or trainers with objective reference when they observe muscle exertion and fatigue. Suggestions on possible improvements were put forward.