Dynamic feature for an effective elbow-joint angle estimation based on electromyography signals

Abstract

<a name="_Hlk561743"></a><span>Some physical parameters influence the electromyography signal (EMG). when the EMG signal is used to estimate the position of the elbow. An adaptable feature was important to reduce a variation on the parameters. The aim of this paper is to estimate the joint position of the elbow using EMG signal based on a dynamic function. The major contribution of this work is that the method proposed is capable of determining the elbow position using the non-pattern (NPR) recognition (PR) method. A Wilson amplitude (WAMP) which used a dynamic threshold was used to reduce the EMG signal. The dynamic threshold was generated from the root mean square (RMS) processor. With the dynamic threshold, the model could adapt to any variations on the independent variables. In order to confirm this opportunity, this work involved ten healthy male subjects to perform an experimental protocol. After a tuning and calibration process, the mean of RMS error and correlation coefficient are 9.83º±1.69º and 0.98±0.01 for a single cycle of motion, 10.39º±1.82º and 0.97±0.01 for a continuous cycle of motion and 15.19º±1.92º and 0.94±0.02 for the arbitrary gesture. For conclusion, the performance of the prediction did not significantly depend on the varying cycle of gesture (p-value>0.05). This study has confirmed that the success of the non-pattern recognition-based prediction of elbow position is adaptable to any different subjects, loads, and speed of motion</span><span>.</span>

Mapping Three Electromyography Signals Generated by Human Elbow and Shoulder Movements to Two Degree of Freedom Upper-Limb Robot Control

“…The method is often used because of its quick and simple implementation. Time-domain features are processed without any signal transformation for the raw EMG signals and evaluated based on the value of signal amplitude that varies over time [1,22,28,30,32,33,37]. Processing of the raw data is critical to remove baseline noises, motion artifact noises, etc.…”

“…On the other hand, nonpattern recognition controls are practical and often used as control schemes. The objective, in this case, is to characterize motion, gripping force, rotation of angles, and others [5,11,18,27,[29][30][31][32].…”

Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning

“…Humanrobot interactions (HRI) are a wide research field. Currently, those systems are used in many applications and researches such as robot control systems [11][12][13][14][15][16][17][18][19][20][21][22][23], medical recognitions and rehabilitations [24][25][26][27], and intelligence assistive devices [5,12,15,[28][29][30][31][32][33][34].…”

“…Machine learning has been used extensively in HGR and other EMG-related studies targeting different functionalities. Several kinds of research focusing particularly on elbow and shoulder movements have been reported in Triwiyanto et al [13], Antuvan et al [14], Martinez et al [16], Hassan, Abou-Loukh, and Ibraheem [19], Young et al [45], Jiang et al [46], and Tsai et al [47], classification of upper limb motion using extreme learning machines by Antuvan et al [39], using Support Vector Machine (SVM) [6,8,19,48], investigation of shoulder muscle activation pattern recognition using machine learning by Jiang et al [46], and detection movements using EMG signal for upper limb exoskeletons in reaching tasks by Trigili et al [49]. These papers verify the suitability of EMG signals for biopotential intelligent robot control.…”