Classification of forearm movements from sEMG time domain features using machine learning algorithms

Jose, Noble; Raj, Retheep; Adithya, P K; Sivanadan, K S

doi:10.1109/tencon.2017.8228118

Cited by 23 publications

(12 citation statements)

References 22 publications

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“…Feature extraction and selection is an important step in locomotion mode pattern recognition. Previous studies have shown sEMG features based on the time domain information and the wavelet transform could achieved considerable accuracy for classifying hand movements and gait events [6,7,11,21]. This paper uses statistic criteria to evaluate the 35 EMG features of time domain, frequency domain and time-frequency domain.…”

Section: Discussionmentioning

confidence: 99%

“…Many extraction techniques, including the time domain features, the frequency domain features, the timefrequency domain features, auto-regression coefficients, and nonlinear features, have been proposed in the last two decades. Due to the low complexity of extraction methods without requiring signal transformation, time domain features and auto-regression coefficients have been used in motion recognition, with focus on real-time performance [11][12][13]. However, human motion and the sEMG data have been shown to be non-stationary in nature.…”

Section: Introductionmentioning

confidence: 99%

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Locomotion Mode Recognition for Walking on Three Terrains Based on sEMG of Lower Limb and Back Muscles

Zhong

Yang

et al. 2021

Sensors

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Gait phase detection on different terrains is an essential procedure for amputees with a lower limb assistive device to restore walking ability. In the present study, the intent recognition of gait events on three terrains based on sEMG was presented. The class separability and robustness of time, frequency, and time-frequency domain features of sEMG signals from five leg and back muscles were quantitatively evaluated by statistical analysis to select the best features set. Then, ensemble learning method that combines the outputs of multiple classifiers into a single fusion-produced output was implemented. The results obtained from data collected from four human participants revealed that the light gradient boosting machine (LightGBM) algorithm has an average accuracy of 93.1%, a macro-F1 score of 0.929, and a calculation time of prediction of 15 ms in discriminating 12 different gait phases on three terrains. This was better than traditional voting-based multiple classifier fusion methods. LightGBM is a perfect choice for gait phase detection on different terrains in daily life.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Locomotion Mode Recognition for Walking on Three Terrains Based on sEMG of Lower Limb and Back Muscles

Zhong

Yang

et al. 2021

Sensors

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“…Next, fast nonstationary is related to the biomechanics of the task. The modification of the frequency content of the signal is affected by the variations in muscle force [14].…”

Section: Feature Extractionmentioning

confidence: 99%

Finger Movement Discrimination of EMG Signals Towards Improved Prosthetic Control using TFD

Shair¹,

Jamaluddin²,

Abdullah³

2020

IJACSA

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Prosthetic is an artificially made as a substitute or replacement for missing part of a body. The function of the missing body part can be replaced by using the prosthesis and it can help disabled people do their activities easily. A myoelectric control system is a fundamental part of modern prostheses. The electromyogram (EMG) signals are used in this system to control the prosthesis movements by taking it from a person's muscle. The problem for the myoelectric control system is when it did not receive the same attention to control fingers due to more dexterous of individual and combined finger control in a signal. Thus, a method to solve the problem of the myoelectric control system by using time-frequency distribution (TFD) is proposed in this paper. The EMG features of the individual and combine finger movements for ten subjects and ten different movements is extracted using TFD, ie. spectrogram. Three machine learning algorithms which are Support Vector Machine (SVM), k-Nearest Neighbor (KNN) and Ensemble Classifier are then used to classify the individuals and combine finger movement based on the extracted EMG feature from the spectrogram. The performance of the proposed method is then verified using classification accuracy. Based on the results, the overall accuracy for the classification is 90% (SVM), 100% (KNN) and 100% (Ensemble Classifier), respectively. The finding of the study could serve as an insight to improve the conventional prosthetic control strategies.

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“…The feature data was trained and identified using SVM Model, and 90% accuracy was achieved in the robotic gripping task. Jose et al [4] used multiple sEMG sensors to acquire sEMG signals from the biceps and triceps at a sampling frequency of 10kHz, and selectively extracted integral electromyogram (iEMG) and Slope Sign Changes (SSC). Multi-layer perceptron neural network and random forest model are used to recognize the internal and external rotation of the arm, and the accuracy is 91.6% and 97.7% respectively.…”

Section: Introductionmentioning

confidence: 99%

Movements Classification of Multi-Channel sEMG Based on CNN and Stacking Ensemble Learning

Shen

Chen

et al. 2019

IEEE Access

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In recent years, the analysis of surface electromyography (sEMG) signals by feature engineering and machine learning has developed rapidly. However, when feature engineering is applied to feature extraction of sEMG signals, important feature information in the signals will inevitably be omitted, which will reduce the performance of signal analysis and recognition. Therefore, this paper proposes a method to complete classification of sEMG hand movements based on convolutional neural network (CNN) and stacking ensemble learning. In this method, a primary classifier based on CNN is designed to extract sEMG data features, which avoid omission of important feature information. A secondary classifier based on the stacking method is designed to integrate three primary classifiers trained with time domain, frequency domain and time-frequency domain data of the sEMG signal respectively. Then, several experiments on NinaPro DB5 dataset is performed to evaluate the proposed models. When the window length is 200ms, primary classifier is trained and tested with the sEMG signal data divided by the 80ms, 100ms, and 125ms sliding length. The best accuracy can reach 71%. The primary classifier and the secondary classifier trained and tested with sEMG signal data divided by window lengths of 200ms and 300ms in the case of a sliding length of 100ms. When the window length is 200ms, the best primary classifier accuracy and the best secondary classifier accuracy can be 70.92% and 72.09%, respectively. On the window length of 300ms, the best primary classifier accuracy and the best secondary classifier accuracy can reach 75.02% and 76.02%, respectively. Finally, the model designed is compared with Linear Discriminant Analysis (LDA), Long Short Term Memory-CNN (LCNN), Support Vector Machine (SVM), and Random Forests. Under the same conditions, the average accuracy of the secondary classifier is 11.5%, 13.6%. and 10.1% higher than LDA, SVM, and LCNN, respectively. Also, the average accuracy rate is 3.05% higher than SVM and Random Forests.INDEX TERMS Surface electromyography, movements classification, convolutional neural network, ensemble learning.

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Classification of forearm movements from sEMG time domain features using machine learning algorithms

Cited by 23 publications

References 22 publications

Locomotion Mode Recognition for Walking on Three Terrains Based on sEMG of Lower Limb and Back Muscles

Locomotion Mode Recognition for Walking on Three Terrains Based on sEMG of Lower Limb and Back Muscles

Finger Movement Discrimination of EMG Signals Towards Improved Prosthetic Control using TFD

Movements Classification of Multi-Channel sEMG Based on CNN and Stacking Ensemble Learning

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