Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network

Zhang, Zhen; Yang, Kuo; Qian, Jinwu; Zhang, Lunwei

doi:10.3390/s19143170

Cited by 130 publications

(79 citation statements)

References 37 publications

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“…Cameras are also used to recognize hand motion [ 9 ], while it is sensitive to the use environment such as background texture, color, and lighting. Surface electromyographic signal (sEMG) is a useful non-intrusive technique for recording the electrical activity produced by muscles through surface sensors placed on the skin, which is a promising candidate for motion detection, gesture recognition and even gesture prediction [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network

Zhang

Yang

2020

Sensors

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Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Most sEMG-based hand gesture recognition, which uses machine learning as the classifier, depends on feature extraction of sEMG data. Recently, a deep leaning-based approach such as recurrent neural network (RNN) has provided a choice to automatically learn features from raw data. This paper presents a novel hand gesture prediction method by using an RNN model to learn from raw sEMG data and predict gestures. The sEMG signals of 21 short-term hand gestures of 13 subjects were recorded with a Myo armband, which is a non-intrusive, low cost, commercial portable device. At the start of the gesture, the trained model outputs an instantaneous prediction for the sEMG data. Experimental results showed that the more time steps of data that were known, the higher instantaneous prediction accuracy the proposed model gave. The predicted accuracy reached about 89.6% when the data of 40-time steps (200 ms) were used to predict hand gesture. This means that the gesture could be predicted with a delay of 200 ms after the hand starts to perform the gesture, instead of waiting for the end of the gesture.

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Section: Introductionmentioning

confidence: 99%

A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network

Zhang

Yang

2020

Sensors

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“…Another critical step of human motion classification is the selection of classification technology. Based on the above feature extraction methods, researchers mainly used support a vector machine (SVM), decision tree (DT), random forest (RF), nearest neighbor (KNN), and naive Bayes (NB) to classify human motion [ 27 , 28 , 29 , 30 , 31 , 32 ]. Rohit Gupta et al used a time-domain analysis method to classify the movement of the lower limbs, and concluded that the linear discriminant analysis (LDA) classifier had the highest accuracy, and for different feature subsets, the classification accuracy was between 89% and 99% [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal

Qin

Shi

2020

Entropy

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The real-time and accuracy of motion classification plays an essential role for the elderly or frail people in daily activities. This study aims to determine the optimal feature extraction and classification method for the activities of daily living (ADL). In the experiment, we collected surface electromyography (sEMG) signals from thigh semitendinosus, lateral thigh muscle, and calf gastrocnemius of the lower limbs to classify horizontal walking, crossing obstacles, standing up, going down the stairs, and going up the stairs. Firstly, we analyzed 11 feature extraction methods, including time domain, frequency domain, time-frequency domain, and entropy. Additionally, a feature evaluation method was proposed, and the separability of 11 feature extraction algorithms was calculated. Then, combined with 11 feature algorithms, the classification accuracy and time of 55 classification methods were calculated. The results showed that the Gaussian Kernel Linear Discriminant Analysis (GK-LDA) with WAMP had the highest classification accuracy rate (96%), and the calculation time was below 80 ms. In this paper, the quantitative comparative analysis of feature extraction and classification methods was a benefit to the application for the wearable sEMG sensor system in ADL.

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“…The armband can recognize five kinds of hand movements, including palm extension, making a fist, turning wrist outwards, turning wrist inwards, thumb and middle finger two consecutive clicking. This sEMG sensor is equipped with eight medical grade stainless steel dry electrodes that can achieve eight-channel sEMG data acquisition [3] [4]. This device is easy to use.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Subtle Gestures by 2-Channel sEMG Using Parameter Estimation Classifiers Based on Probability Density

Liang

Chen

et al. 2020

IEEE Access

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The abundant movement information carried by the surface electromyography (sEMG) signal can comprehensively reflect the muscle movement patterns on the surface of the human body, and the characteristics of physiological signals involving movement information have promoted the vigorous development in the field of human-computer interaction in machine learning. This paper takes subtle gestures as the research objects, and proposes a subtle gestures recognition system that uses two-channel sEMG signal and utilizes Quadratic Discriminant Analysis (QDA) and Linear Discriminant Analysis (LDA) which are parameter estimation classifiers based on probability density. First, combining the raw sEMG signal and the envelope sEMG signal, the number of channels for sensors and corresponding position of the muscle groups are preferably selected. Then, we propose to use the Pearson correlation coefficient to optimize the four types of features extracted in the time domain, frequency domain, time-frequency domain and AR model parameters, which effectively ensures the uniqueness of each gesture and eliminates individual differences. Finally, this paper compares four classifiers of QDA, LDA, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), and explores the pattern recognition classifier suitable for subtle actions. The experiment collects 9 kinds of subtle gestures from 8 subjects. The results show that the system can use only two channels of sEMG signal to recognize 9 kinds of subtle gestures which include multifinger clicking or pinching or holding objects, and the most suitable classifiers QDA and LDA have the average recognition rates of 95.79% and 95.01%, respectively.

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Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network

Cited by 130 publications

References 37 publications

A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network

A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network

Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal

Recognition of Subtle Gestures by 2-Channel sEMG Using Parameter Estimation Classifiers Based on Probability Density

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