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Deep back propagation–long short-term memory network based upper-limb sEMG signal classification for automated rehabilitation

Wang Ying, Wu Qun, Dey Nilanjan, Fong Simon, Ashour Amira S.

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 3

987--1001

Autonomous rehabilitation training for assisted patients with injured upper-limbs promotes the regenerative communication between muscle signals and brain consciousness. Surface electromyographic (sEMG) is a type of electrical signals of neuromuscular activity recorded by electrodes on the surface of the human body, which is widely applied for detecting gestures and stimuli reactions. Experimental results proved the importance of the sEMG signals for extracting such reactions, in which, the segmentation and classification of the sEMG are vital tasks. The objective of the present work is to segment and classify the sEMG signals of patients to assist the design of clinical rehabilitation devices based on the classification of sEMG signals. In the pre-processing stage, a dual-tone multi-frequency signaling is designed for signal coding; subsequently, the pre-processed sEMG signal is transformed by the Fast Fourier Transfer. Afterward, a time-series frequency analysisis performed by applyingHiddenMarkov Models.A basic traditional longshort- term memory (LSTM) model is addressed for waveform-based classification to be compared to the proposed improved deep BP (back-propagation)–LSTM for sEMG signal classification. Seventeen performance features are selected for evaluating the proposed multi-classification, deep learning model for classifying six actions, namely moving gesture of grip, slowly moving, flexor, straight lift, stretch, and up-high lift; which were proposed by rehabilitation physician. The experiment results indicated the superiority of the proposed method compared to other well-known classifiers, such as the neural network, support vector machine, decision trees, Bayes inference, and recurrent neural network. The proposed deep BP–LSTM network achieved 92% accuracy, 89% specificity, 91% precision, and 96% F1-score, in the multi-classification of the sEMG signals.

Wpływ typu mięśnia na parametry widma mocy sygnału EMG

Bartuzi P., Roman-Liu D.

Aktualne Problemy Biomechaniki

2008

z. 2

7--12

Celem pracy było opracowanie wskaźników, opartych na widmie mocy sygnału elektromiograficznego (EMG), obrazujących budowę morfologiczną mięśnia. Sygnał EMG został zarejestrowany z mięśni: czworoboczny część zstępująca (trapezius pars descendens-TPD), naramienny część obojczykowa (deltoideus pars clavicularis-DPC), naramienny część barkowa (deltoideus pars acromialis-DPA) oraz zginacz łokciowy nadgarstka (flexor carpi ulnaris-FCU), podczas badań w 6 wariantach obciążenia. W wyniku analizy opracowano parametry widma mocy sygnału EMG, szczególnie wrażliwe na zmiany w strukturze mięśnia.

The aim of this study was to develop coefficients of electromyographic (EMG) signal, which show the morphological properties of muscle. Measurements of EMG signal from muscles: trapezius pars descendens (TPD), deltoideus pars clavicularis (DPC), deltoideus pars acromialis (DPA) and flexor carpi ulnaris (FCU), during research in 6 variants of load were realized. As the result of analysis the parameters based on power spectrum of EMG signal were worked out. Differences between morphological structure of muscle can be described using these parameters.