伴隨著人體肌肉的收縮，肌電訊號會與收縮程度成正相關，並可在人體皮膚表面量測之。除肢體完全殘缺者外，運用此種肌肉收縮伴隨肌電訊號產生的特性，可以用來設計輔具的輸出參考，使輔具可以即時輸出輔助力矩於特定部位。因而此方向的輔具設計屬於「強化使用者體能型」，而非為肢體殘缺者設計的「義肢型」。
本實驗室先前有關肌電訊號應用於人體輔具之研究是以「壓力鞋-角度計模組」配合肌電訊號的運算結果，兩者互相輔助，即時對使用者作出回饋。但亦無法跳脫模組化之應用，僅能對單一動作識別。故本文藉由肌電訊號的本質、反動力學及肌肉機械學模型之探討，確立下肢肌電訊號與人體膝蓋力矩具有相關性，藉由Adaptive Neuro-Fuzzy Inference System(ANFIS)建立下肢肌電訊號與人體膝蓋力矩之模型，將肌電訊號即時換算輔具的參考力矩，給予輔具控制命令施加於膝蓋上，以期令使用者在任何膝蓋彎曲角度下，感覺較未穿輔具時輕鬆。 又反動態學之應用過於複雜，即使以簡化之靜態反動力學推算膝蓋力矩亦不夠直觀，因此本文設計一線性扭簧-角度計模組來推算力矩，作為模型學習之力矩輸入。最後輔以實驗來驗證其可行性以及實際與輔具整合之成果。

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