Deep Neural Network Approach for Hand, Wrist, Grasping and Functional Movements Classification using Low-cost sEMG Sensors

Chaiyaroj, Attawit; Sri-iesaranusorn, Panyawut; Buekban, Chatchai; Dumnin, Songphon; Thanawattano, Chusak; Surangsrirat, Decho

doi:10.1109/bibm47256.2019.8983049

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…There is, however, no report on the balanced accuracy. Additionally, a previous study published by our team achieved a balanced accuracy of 77.00% for 1,000 ms window size (Chaiyaroj et al, 2019 ). With an improvement in the feature extraction and parameter tuning procedures, we achieved a balanced accuracy of 84.00%, observing an increase in performance of 7.00%.…”

Section: Resultsmentioning

confidence: 57%

“…Original studies of the datasets are shown as baselines. For DB5, Pizzolato (Chaiyaroj et al, 2019). With an improvement in the feature extraction and parameter tuning procedures, we achieved a balanced accuracy of 84.00%, observing an increase in performance of 7.00%.…”

Section: Classification Performance Analysismentioning

confidence: 87%

“…A deep neural network (DNN) has been chosen for dealing with real-world signal processing tasks, due to its outstanding performance compared to other machine learning algorithms (Park and Lee, 2016;Chen et al, 2017;Orjuela-Cañón et al, 2017;Tsinganos et al, 2018;Chaiyaroj et al, 2019). Motivated by this fact and considering our aim for a real-time system, we implemented a simple feed-forward neural network model.…”

Section: Deep Neural Network Classifiermentioning

confidence: 99%

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Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network

Sri-iesaranusorn

Chaiyaroj

Buekban

et al. 2021

Front. Bioeng. Biotechnol.

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Surface electromyography (sEMG) is a non-invasive and straightforward way to allow the user to actively control the prosthesis. However, results reported by previous studies on using sEMG for hand and wrist movement classification vary by a large margin, due to several factors including but not limited to the number of classes and the acquisition protocol. The objective of this paper is to investigate the deep neural network approach on the classification of 41 hand and wrist movements based on the sEMG signal. The proposed models were trained and evaluated using the publicly available database from the Ninapro project, one of the largest public sEMG databases for advanced hand myoelectric prosthetics. Two datasets, DB5 with a low-cost 16 channels and 200 Hz sampling rate setup and DB7 with 12 channels and 2 kHz sampling rate setup, were used for this study. Our approach achieved an overall accuracy of 93.87 ± 1.49 and 91.69 ± 4.68% with a balanced accuracy of 84.00 ± 3.40 and 84.66 ± 4.78% for DB5 and DB7, respectively. We also observed a performance gain when considering only a subset of the movements, namely the six main hand movements based on six prehensile patterns from the Southampton Hand Assessment Procedure (SHAP), a clinically validated hand functional assessment protocol. Classification on only the SHAP movements in DB5 attained an overall accuracy of 98.82 ± 0.58% with a balanced accuracy of 94.48 ± 2.55%. With the same set of movements, our model also achieved an overall accuracy of 99.00% with a balanced accuracy of 91.27% on data from one of the amputee participants in DB7. These results suggest that with more data on the amputee subjects, our proposal could be a promising approach for controlling versatile prosthetic hands with a wide range of predefined hand and wrist movements.

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Section: Resultsmentioning

confidence: 57%

Section: Classification Performance Analysismentioning

confidence: 87%

Section: Deep Neural Network Classifiermentioning

confidence: 99%

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Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network

Sri-iesaranusorn

Chaiyaroj

Buekban

et al. 2021

Front. Bioeng. Biotechnol.

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“…Recently, many researchers have paid more attention to deep learning in the field of EMG pattern recognition. It can automatically learn features of different abstract levels from many input samples, thereby avoiding cumbersome feature extraction and optimization processes and realizing end-to-end EMG gesture recognition (Weng et el., 2021;Su et al, 2021;Tsinganos et al, 2019;Chaiyaroj et al, 2019). Atzori et al (2016) proposed a LeNet-based convolutional neural network model AtzoriNet for end-to-end EMG gesture recognition.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Gesture Recognition Using Surface EMG Signals Based on Multi-Stream Residual Network

Yang

Sun

et al. 2021

Front. Bioeng. Biotechnol.

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Gesture recognition technology is widely used in the flexible and precise control of manipulators in the assisted medical field. Our MResLSTM algorithm can effectively perform dynamic gesture recognition. The result of surface EMG signal decoding is applied to the controller, which can improve the fluency of artificial hand control. Much current gesture recognition research using sEMG has focused on static gestures. In addition, the accuracy of recognition depends on the extraction and selection of features. However, Static gesture research cannot meet the requirements of natural human-computer interaction and dexterous control of manipulators. Therefore, a multi-stream residual network (MResLSTM) is proposed for dynamic hand movement recognition. This study aims to improve the accuracy and stability of dynamic gesture recognition. Simultaneously, it can also advance the research on the smooth control of the Manipulator. We combine the residual model and the convolutional short-term memory model into a unified framework. The architecture extracts spatiotemporal features from two aspects: global and deep, and combines feature fusion to retain essential information. The strategy of pointwise group convolution and channel shuffle is used to reduce the number of network calculations. A dataset is constructed containing six dynamic gestures for model training. The experimental results show that on the same recognition model, the gesture recognition effect of fusion of sEMG signal and acceleration signal is better than that of only using sEMG signal. The proposed approach obtains competitive performance on our dataset with the recognition accuracies of 93.52%, achieving state-of-the-art performance with 89.65% precision on the Ninapro DB1 dataset. Our bionic calculation method is applied to the controller, which can realize the continuity of human-computer interaction and the flexibility of manipulator control.

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Performance Comparison of Different Classifiers Applied to Gesture Recognition from sEMG Signals

Sgambato

Castellano

2022

XXVII Brazilian Congress on Biomedical Engineering

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Deep Neural Network Approach for Hand, Wrist, Grasping and Functional Movements Classification using Low-cost sEMG Sensors

Cited by 7 publications

References 18 publications

Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network

Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network

Dynamic Gesture Recognition Using Surface EMG Signals Based on Multi-Stream Residual Network

Performance Comparison of Different Classifiers Applied to Gesture Recognition from sEMG Signals

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