Surface electromyography–based hand movement recognition using the Gaussian mixture model, multilayer perceptron, and AdaBoost method

Zhou, Shengli; Yin, Kuiying; Fei, Fei; Zhang, Ke

doi:10.1177/1550147719846060

Cited by 18 publications

(9 citation statements)

References 39 publications

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“…Here we conduct a comparative study to compare the proposed heterogeneously dilated model with conventional non-sequential Deepnets that have been most commonly used in the literature [10]–[16],[43],[44]. This comparative study emphasizes the importance of sequential modeling in capturing the underlying temporal dynamics.…”

Section: Comparative Studymentioning

confidence: 99%

Deep Heterogeneous Dilation of LSTM for Transient-phase Gesture Prediction through High-density Electromyography: Towards Application in Neurorobotics

Sun

Libby

et al. 2021

Preprint

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Deep networks have been recently proposed to estimate motor intention using conventional bipolar surface electromyography (sEMG) signals for myoelectric control of neurorobots. In this regard, deepnets are generally challenged by long training times (affecting the practicality and calibration), complex model architectures (affecting the predictability of the outcomes), a large number of trainable parameters (increasing the need for big data), and possibly overfitting. Capitalizing on our recent work on homogeneous temporal dilation in a Recurrent Neural Network (RNN) model, this paper proposes, for the first time, heterogeneous temporal dilation in an LSTM model and applies that to high-density surface electromyography (HD-sEMG), allowing for decoding dynamic temporal dependencies with tunable temporal foci. In this paper, a 128-channel HD-sEMG signal space is considered due to the potential for enhancing the spatiotemporal resolution of human-robot interfaces. Accordingly, this paper addresses a challenging motor intention decoding problem of neurorobots, namely, transient intention identification. The aforementioned problem only takes into account the dynamic and transient phase of gesture movements when the signals are not stabilized or plateaued, addressing which can significantly enhance the temporal resolution of human-robot interfaces. This would eventually enhance seamless real-time implementations. Additionally, this paper introduces the concept of “dilation foci” to modulate the modeling of temporal variation in transient phases. In this work a high number (i.e. 65) of gestures is included, which adds to the complexity and significance of the understudied problem. Our results show state-of-the-art performance for gesture prediction in terms of accuracy, training time, and model convergence.

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Section: Comparative Studymentioning

confidence: 99%

Deep Heterogeneous Dilation of LSTM for Transient-phase Gesture Prediction through High-density Electromyography: Towards Application in Neurorobotics

Sun

Libby

et al. 2021

Preprint

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“…The strength features of sEMG signals can be obtained by selecting absolute integral, average, square root, root mean square, zero cross ratio, and so on. 26 In a previous study, researchers integrated the lower limb exoskeleton and a Smart Walker into one system for the rehabilitation of patients' gait nerve. sEMG-based pattern classification on "sitting/standing" and knee "bending/stretching" was designed to control this hybrid robot system.…”

Section: Introductionmentioning

confidence: 99%

Design and evaluation of a surface electromyography-controlled lightweight upper arm exoskeleton rehabilitation robot

Liu

Zhu

et al. 2021

International Journal of Advanced Robotic Systems

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Nowadays, the rehabilitation robot has been developed for rehabilitation therapy. However, there are few studies on upper arm exoskeletons for rehabilitation training of muscle strength. This article aims to design a surface electromyography-controlled lightweight exoskeleton rehabilitation robot for home-based progressive resistance training. The exoskeleton’s lightweight structure is designed based on the kinematic model of the elbow joint and ergonomics sizes of the arm. At the same time, the overall weight of the exoskeleton is controlled at only 3.03 kg. According to the rehabilitation training task, we use torque limit mode to ensure stable torque output at variable velocity. We also propose a surface electromyography-based control method, which uses k- Nearest Neighbor algorithm to classify surface electromyographic signals under progressive training loads, and utilizes principal component analysis to improve the recognition accuracy to control the exoskeleton to provide muscle strength compensation. The assessment experiment of the exoskeleton rehabilitation robot shows that the dynamic recognition accuracy of this control method is 80.21%. Muscle activity of biceps brachii and triceps brachii under each training load decreases significantly when subjects with the exoskeleton robot. The results indicate that the exoskeleton rehabilitation robot can output the corresponding torque to assist in progressive resistance training. This study provides a solution to potential problems in the family-oriented application of exoskeleton rehabilitation robots.

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“…Osama dorgham et al used time-domain features (such as MAV, RMS, VAR, and STD) to estimate muscle strength under different loads [ 15 ]. Shengli Zhou et al used the frequency domain analysis method of median frequency (MDF) and peak frequency (PKF) to extract features, and combined with the Gaussian model, the accuracy of motion classification reached 89.5% [ 16 ]. Erdem Yavuz et al extracted sEMG signal features by calculating Mel-Frequency Cepstral Coefficients (MFCCs) for basic motion classification [ 17 ].…”

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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Surface electromyography–based hand movement recognition using the Gaussian mixture model, multilayer perceptron, and AdaBoost method

Cited by 18 publications

References 39 publications

Deep Heterogeneous Dilation of LSTM for Transient-phase Gesture Prediction through High-density Electromyography: Towards Application in Neurorobotics

Deep Heterogeneous Dilation of LSTM for Transient-phase Gesture Prediction through High-density Electromyography: Towards Application in Neurorobotics

Design and evaluation of a surface electromyography-controlled lightweight upper arm exoskeleton rehabilitation robot

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

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