MyoNet: A Transfer-Learning-Based LRCN for Lower Limb Movement Recognition and Knee Joint Angle Prediction for Remote Monitoring of Rehabilitation Progress From sEMG

Gautam, Arvind; Panwar, Madhuri; Biswas, Dwaipayan; Acharyya, Amit

doi:10.1109/jtehm.2020.2972523

Cited by 82 publications

(69 citation statements)

References 33 publications

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“…The input EMG signals have three positions namely walking as W, standing as ST, and sitting as SI on normal/healthy and knee pathology movements. In this section, the performance of the proposed CNN is tested with MyoNet [ 18 ] on both normal and abnormal knee movements for 11 subjects. Table 3 presents the validated results of both techniques on healthy 11 subjects based on three different positions.…”

Section: Resultsmentioning

confidence: 99%

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Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients

Subramani¹,

Srinivas

et al. 2021

Pers Ubiquit Comput

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Many Coronavirus disease 2019 (COVID-19) and post-COVID-19 patients experience muscle fatigues. Early detection of muscle fatigue and muscular paralysis helps in the diagnosis, prediction, and prevention of COVID-19 and post-COVID-19 patients. Nowadays, the biomedical and clinical domains widely used the electromyography (EMG) signal due to its ability to differentiate various neuromuscular diseases. In general, nerves or muscles and the spinal cord influence numerous neuromuscular disorders. The clinical examination plays a major role in early finding and diagnosis of these diseases; this research study focused on the prediction of muscular paralysis using EMG signals. Machine learning–based diagnosis of the diseases has been widely used due to its efficiency and the hybrid feature extraction (FE) methods with deep learning classifier are used for the muscular paralysis disease prediction. The discrete wavelet transform (DWT) method is applied to decompose the EMG signal and reduce feature degradation. The proposed hybrid FE method consists of Yule-Walker, Burg’s method, Renyi entropy, mean absolute value, min-max voltage FE, and other 17 conventional features for prediction of muscular paralysis disease. The hybrid FE method has the advantage of extract the relevant features from the signals and the Relief-F feature selection (FS) method is applied to select the optimal relevant feature for the deep learning classifier. The University of California, Irvine (UCI), EMG-Lower Limb Dataset is used to determine the performance of the proposed classifier. The evaluation shows that the proposed hybrid FE method achieved 88% of precision, while the existing neural network (NN) achieved 65% of precision and the support vector machine (SVM) achieved 35% of precision on whole EMG signal.

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Section: Resultsmentioning

confidence: 99%

“…Gautam et al [ 18 ] developed a transfer learning–based long-term recurrent convolution network named as MyoNet. The method involves three processes namely FE, prediction of joint angle, and classification of movement.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients

Subramani¹,

Srinivas

et al. 2021

Pers Ubiquit Comput

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“…As far as the author knew, this algorithm was the first time applied to the lower limb motion classification. In many studies, the lower limb motion classification accuracy was improved by increasing the number of electrodes [7,9,11,13,14,29]. However, in the actual lower limb movement process, the sEMG sensor will be disturbed by noise, the more sensors will lead to the instability for classification accuracy, and the wearer will also feel uncomfortable.…”

Section: Discussionmentioning

confidence: 99%

“…In the process of the sEMG feature extraction, many researchers used the time or frequency analysis methods to extract feature vectors from sEMG signals [9][10][11][12][13]. For example, sEMG amplitude, root mean square (RMS), zerocrossing (ZC), autoregressive-coefficient, mean absolute value (MAV), fourier transform coefficient, cepstrumcoefficients, peak frequency, and median frequency analysis methods [14,15].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Classification of Lower Limb Motion Based on sEMG Signals

et al. 2020

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Surface electromyography (sEMG) signals can reflect the body motion information and are widely used in military, medical rehabilitation, industrial production. The lower limb motion classification mainly includes feature extraction and classification model establishment. Firstly, we proposed a feature extraction method based on the wavelet packet transform (WPT) and principal component analysis (PCA). We used the wavelet packet method to decompose the sEMG signals of three muscles in the lower limb and got the 24-dimensional eigenvector. To reduce the calculation and improve the speed of the classification model, we used the PCA method to reduce the dimension of the feature vector and got the 3-dimensional eigenvector. Then, we proposed a method based on the scale unscented Kalman filter (SUKF) and neural network (NN) for lower limb motion classification. Through the scale correction unscented transform (SCUT) could optimize the neural network weight and improve lower limb motion classification accuracy. Finally, the experimental results showed that the average accuracy was 93.7%. Compared with the backpropagation neural network (BPNN) and wavelet neural network (WNN), this method could improve the accuracy and reliability of the lower limb motion classification. INDEX TERMS sEMG signals, lower limb motion classification, feature extraction, wavelet packet transform, principal component analysis, unscented Kalman filter, neural networks.

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“…The myoelectric controlled powered prosthetic hands and limbs have the potential for improving the quality of life of amputated subjects [1] – [6] . The control of such prosthetic hands is facilitated by classifying various movements (e.g.…”

Section: Introductionmentioning

confidence: 99%

Locomo-Net: A Low -Complex Deep Learning Framework for sEMG-Based Hand Movement Recognition for Prosthetic Control

Gautam

Panwar

Wankhede

et al. 2020

IEEE J. Transl. Eng. Health Med.

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Background: The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named 'Low-Complex Movement recognition-Net' (LoCoMo-Net) built with convolution neural network (CNN) for recognition of wrist and finger flexion movements; grasping and functional movements; and force pattern from single channel surface electromyography (sEMG) recording. The network consists of a two-stage pipeline: 1) input data compression; 2) data-driven weight sharing. Methods: The proposed framework was validated on two different datasets-our own dataset (DS1) and publicly available NinaPro dataset (DS2) for 16 movements and 50 movements respectively. Further, we have prototyped the proposed LoCoMo-Net on Virtex-7 Xilinx field-programmable gate array (FPGA) platform and validated for 15 movements from DS1 to demonstrate its feasibility for real-time execution. Results: The effectiveness of the proposed LoCoMo-Net was verified by a comparative analysis against the benchmarked models using the same datasets wherein our proposed model outperformed Twin-Support Vector Machine (SVM) and existing CNN based model by an average classification accuracy of 8.5 % and 16.0 % respectively. In addition, hardware complexity analysis is done to reveal the advantages of the two-stage pipeline where approximately 27 %, 49 %, 50 %, 23 %, and 43 % savings achieved in lookup tables (LUT's), registers, memory, power consumption and computational time respectively. Conclusion: The clinical significance of such sEMG based accurate and lowcomplex movement recognition system can be favorable for the potential improvement in quality of life of an amputated persons.

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MyoNet: A Transfer-Learning-Based LRCN for Lower Limb Movement Recognition and Knee Joint Angle Prediction for Remote Monitoring of Rehabilitation Progress From sEMG

Abstract: This work was supported by the project entitled ''Iot based deMonstrator design using proposed methOdology with CNN and BSS for RehabIlitated ParaLYZEd Patients (i-MOBILYZE)'' funded by the Xilinx Inc., USA with the grant number IITH/EE/F091/S81.

Cited by 82 publications

References 33 publications

Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients

Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients

Feature Extraction and Classification of Lower Limb Motion Based on sEMG Signals

Locomo-Net: A Low -Complex Deep Learning Framework for sEMG-Based Hand Movement Recognition for Prosthetic Control

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