The Fatigue Analysis for Early Warning System based on Fuzzy Algorithm - The Calf as the example

• Main Authors: Hang-Yan-Lin, 林宏諺
• Other Authors: Jui-Chuan Cheng
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/p3czf3

碩士 === 國立高雄應用科技大學 === 電子工程系碩士班 === 104 === Muscle fatigue is one of the main factors influencing the sports performance. Normally the exercise-induced injuries depends on distance, reiterating exercise, excess training without proper resting and accumulating pressure to let our bone overburden. Under those circumstances runners need a long period of time to recover. Using electromyography (EMG) recorded surface myoelectric activity to analyze muscle fatigue has the advantages of simplicity, convenience, and without intrusion which is widely used by sports science. In this thesis, the fatigue analysis for early warning system based on fuzzy algorithm is developed. The system does not need to construct complex mathematical functions , based on the participants’ EMG, heart rate and exercise time after exercise to calculate fatigue level , and display the results in the smart phone , to remind campaigners whether to stop or continue. The experimental results show that, when the sporters have reached the fatigue warning level, the proposed system can alert and display the fatigue status, immediate response to the sporters through the intelligent devices and make expert recommendations, thus reduce the incidence of exercise-induced injuries.