Chen Szi-Wen, Liaw Jiunn-Woei, Chan Hsiao-Lung, Chang Ya-Ju, Ku Chia-Hao
Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan.
Healthy Aging Research Center (HARC), Chang Gung University, Taoyuan 333, Taiwan.
Sensors (Basel). 2014 Jul 10;14(7):12410-24. doi: 10.3390/s140712410.
A real-time muscle fatigue monitoring system was developed to quantitatively detect the muscle fatigue of subjects during cycling movement, where a fatigue progression measure (FPM) was built-in. During the cycling movement, the electromyogram (EMG) signals of the vastus lateralis and gastrocnemius muscles in one leg as well as cycling speed are synchronously measured in a real-time fashion. In addition, the heart rate (HR) and the Borg rating of perceived exertion scale value are recorded per minute. Using the EMG signals, the electrical activity and median frequency (MF) are calculated per cycle. Moreover, the updated FPM, based on the percentage of reduced MF counts during cycling movement, is calculated to measure the onset time and the progressive process of muscle fatigue. To demonstrate the performance of our system, five young healthy subjects were recruited. Each subject was asked to maintain a fixed speed of 60 RPM, as best he/she could, under a constant load during the pedaling. When the speed reached 20 RPM or the HR reached the maximal training HR, the experiment was then terminated immediately. The experimental results show that the proposed system may provide an on-line fatigue monitoring and analysis for the lower extremity muscles during cycling movement.
开发了一种实时肌肉疲劳监测系统，用于在骑行运动期间定量检测受试者的肌肉疲劳，该系统内置了疲劳进展测量值（FPM）。在骑行运动过程中，实时同步测量一条腿的股外侧肌和腓肠肌的肌电图（EMG）信号以及骑行速度。此外，每分钟记录心率（HR）和伯格主观用力程度量表值。利用EMG信号，计算每个周期的电活动和中位频率（MF）。此外，基于骑行运动期间MF计数减少的百分比计算更新后的FPM，以测量肌肉疲劳的发作时间和进展过程。为了证明我们系统的性能，招募了五名年轻健康的受试者。要求每个受试者在蹬踏过程中在恒定负荷下尽可能保持60转/分钟的固定速度。当速度达到20转/分钟或心率达到最大训练心率时，实验立即终止。实验结果表明，所提出的系统可以为骑行运动期间的下肢肌肉提供在线疲劳监测和分析。
