以肌電波為基礎之機器手臂運動控制

Abstract

肌電波（electromyography，EMG)是肌肉收縮過程所產生的生理訊號，含有肌肉收縮之強度與動作之意圖，可作為肢體殘障與年長人士的義肢或機器手臂的控制命令。為建構一套以肌電波為基礎之機器手臂運動操控系統，本論文提出一個簡單又有效率之方法，起始點偵測法 (initial point detection)，其原理為當擷取之特徵(feature)值高於上臨界值時則判定肌肉是處於活動之狀態，直至特徵值低於下臨界值時才判定肌肉停止活動。然個體之模糊性，致使上下臨界值之設定不易，雖由嘗試錯誤法(trial-and-error method)可使分類器達到高的辨識率，但選取過程費工耗時，故引用模糊系統加以改善，其可行性經單自由度(one degree of freedom)機器手臂運動控制獲得確認。然將所提之架構延伸於辨識控制多自由度機器手臂運動之肌電訊號時，發現原方法並不適用於肌肉相互干涉(muscle mutual interference)過大的情況，進而採用希爾伯特-黃轉換法(Hilbert-Huang transform)中之經驗模態分解法(empirical mode decomposition, EMD)，將EMG訊號分解成多個內建模態函數(instrinsic mode function, IMF)，萃取出主要執行運動肌肉之訊號。同時為有效地獲得多個肌肉其上下臨界值合適之歸屬函數，本論文應用適應性類神經模糊推論系統(adaptive neuro-fuzzy inference system, ANFIS)改善傳統模糊系統要由人工調整的缺點。所提之方法既沒有複雜之演算流程，也不須學習與訓練過程，故即時之肌電波機器手臂運動控制得以實現。

Electromyography (EMG) signal, as a physiological signal generated during muscle contraction, implicates several important messages, such as the muscular force level and operator’s intention. It is very suitable to serve as the control signal for the manipulation of the rehabilitation device, human-assisting robot and others. To develop an effective robot motion governing based on using EMG signal, this dissertation proposes a so-called initial point detection method to discriminate the up limb motion onset by detecting the instant when the magnitude of the extracted EMG feature reaches the upper critical value and offset when that descends to the lower critical value from onset state. Consequently, the mapping between the limb EMG signals and the corresponding robot arm movements can be established very quickly. Meanwhile, due to the individual fuzziness, the tuning of the system parameters for the individual user is not that straightforward. Thus the concept of the fuzzy system is employed so that the tedious process encountered in the trial-and-error method can be avoided. While the proposed system is shown to be effective for robot motion governing, it is not appropriate to serve as a classifier for more than 1-DOF (degree of freedom) limb motion as it has larger muscle mutual interference. To tackle this, the EMD is applied to decompose the EMG signals into several intrinsic mode functions (IMFs). Each IMF represents different physical characteristic, so that the major muscular movements can be recognized. Meanwhile, for multi-DOF limb motion, the fuzzy system adopted for 1-DOF motion is not efficient enough for the tuning of the critical values for each individual user. For its excellence on adaptation, the adaptive neuro-fuzzy inference system (ANFIS) is employed to realize the fuzzy system. Because neither complicated computation nor training and learning processes are needed, the proposed scheme not only simplifies system complexity, but also increases the efficiency in motion governing.