Analysis of surface electromyography signal features on osteomyoplastic transtibial amputees for pattern recognition control architectures

Garikayi, Talon; Heever, Dawie van den; Matope, Stephen

doi:10.1016/j.bspc.2017.09.007

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…The estimation of separation vector can be obtained through iteration step using natural gradient descent algorithm [54]. Then the estimation of spike trains of the jth motor unit can be calculated depending on equation (5). After the jth MUST is extracted, the repeating procedure of MUST extraction is conducted on the residue signal until the root mean square value of residue signal is lower than the threshold set manually.…”

Section: Emg Decompositionmentioning

confidence: 99%

“…For example, Englehart et al extracted four time-domain features from the EMG and classified them with linear discriminative analysis [4] reaching classification error rates <6% for four motions. State of the art PR approaches now allow error rates <5% for the classification of >10 motions [5,6]. However, despite high accuracy, classification of the EMG has proven to be not robust to real-life factors such as sweating, electrode misplacement and limb posture [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of finger kinematics from discharge timings of motor units: implications for intuitive control of myoelectric prostheses

et al. 2019

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Objective. The aim of the study was to characterize the accuracy in the identification of motor unit discharges during natural movements using high-density electromyography (EMG) signals and to investigate their correlation with finger kinematics. Approach. High-density EMG signals of forearm muscles and finger joint angles were recorded concurrently during hand movements of ten able-bodied subjects. EMG signals were decomposed into motor unit spike trains (MUSTs) with a blind-source separation method. The first principle component (FPC) of the low-pass filtered MUST was correlated with finger joint angles. Main results. On average, motor units were identified during each individual finger task with an estimated decomposition accuracy 85%. The FPC extracted from discharge rates was strongly associated to the joint angles (), and preceded the joint angles on average by ms. Moreover, the FPC outperformed two time-domain features (the EMG envelop and the root mean square of EMG) in estimating joint angles. Significance. These results indicated the possibility of identifying individual motor unit behavior in dynamic natural contractions. Moreover, the strong association between motor unit discharge behaviors and kinematics proves the potential of the approach for the simultaneous and proportional control of prostheses.

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Section: Emg Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of finger kinematics from discharge timings of motor units: implications for intuitive control of myoelectric prostheses

et al. 2019

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“…The main purpose of feature extraction is to map complex signals from high dimensional space to low dimensional space by using signal characteristics, and make classification simple and intuitive in data background [47][48][49]. Therefore, after a long period of exploration and research, many related researchers have divided the feature extraction into three main directions: time domain analysis, frequency domain analysis, time frequency analysis [50][51].…”

Section: Feature Extraction Of Surface Emg Signalmentioning

confidence: 99%

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Jiang

et al. 2018

Neural Comput & Applic

103

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In the current development and design of sports rehabilitation equipment or biomimetic prostheses, in addition to pay attention to the development and design of the structure, the more core is how to realize the accurate and effective control of the rehabilitation equipment or intelligent prosthesis, and the current research is based on data process and pattern recognition. This paper designs 9 kinds of actions that can react effectively to the function of the hand and extracts the original EMG signals, which are based on the sEMG of the forearm muscles of human hand movement, and uses the 20 order comb filter and wavelet threshold to preprocess the signal, and uses the root mean square, wavelength and nonlinear characteristics sample entropy in time domain as three eigenvalues to construct the input feature vectors of the subsequent action classifier. Finally, the recognition of the hand movements is realized successfully through GRNN and SVM. The recognition rate is 98.64% in SVM classifier and 96.27% in GRNN classifier. Experimental results show that the SVM classifier is better than the GRNN classifier.

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“…Pattern recognition has been applied to myoelectric control since the 1980s [6]. From then on, various classification models have been investigated to improve the control performance [7], [8], which could discriminate tens of motions or gestures with high accuracy (>95%). However, these two control schemes could only identify the discrete motions.…”

Section: Introductionmentioning

confidence: 99%

Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric Control

Chen

Sheng

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Objective. The surface electromyography (EMG) decomposition techniques provide access to motor neuron activities and have been applied to myoelectric control schemes. However, the current decomposition-based myoelectric control mainly focuses on discrete gestures or single-DoF continuous movements. In this study, we aimed to map the motor unit discharges, which were identified from high-density surface EMG, to the three degrees of freedom (DoFs) wrist movements. Methods. The 3-DoF wrist torques and high-density surface EMG signals were recorded concurrently from eight able-bodied subjects. The experimental protocol included single-DoF movements and their various combinations. We decoded the motor unit discharges from the EMG signals using a segment-wise decomposition algorithm. Then the neural features were extracted from motor unit discharges and projected to wrist torques with a multiple linear regression model. We compared the performance of two neural features (twitch model and spike counting) and two training schemes (single-DoF and multi-DoF training). Main results. On average, 145±33 motor units were identified from each subject, with a pulse-to-noise ratio of 30.8±4.2 dB. Both neural features exhibited high estimation accuracy of 3-DoF wrist torques, with an average R 2 of 0.76±0.12 and normalized root mean square error of 11.4±3.1%. Conclusion and Significance. These results demonstrated the efficiency of the proposed method in continuous estimation of 3-DoF wrist torques, which has the potential to advance dexterous myoelectric control based on neural information.

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Analysis of surface electromyography signal features on osteomyoplastic transtibial amputees for pattern recognition control architectures

Cited by 15 publications

References 18 publications

Prediction of finger kinematics from discharge timings of motor units: implications for intuitive control of myoelectric prostheses

Prediction of finger kinematics from discharge timings of motor units: implications for intuitive control of myoelectric prostheses

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric Control

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