Gesture recognition based on surface electromyography<scp>‐feature</scp>image

Cheng, Yangwei; Li, Gongfa; Yu, Mingchao; Du, Jiang; Yun, Juntong; Liu, Ying; Liu, Yibo; Chen, Disi

doi:10.1002/cpe.6051

Cited by 82 publications

(55 citation statements)

References 65 publications

(121 reference statements)

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“…Many excellent image analysis and recognition algorithms have been proposed. For example, the Alexnet which is designed by Srivastava [ 25 ] has achieved the best recognition performance in the Imagenet, an image recognition competition [ 26 , 27 ]. Different from traditional methods of artificial design features, deep learning automatically extracts features through convolution neural network.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Dynamic Gesture Recognition Algorithm Based on 3D Convolutional Neural Network

Liu

Duan

et al. 2021

Computational Intelligence and Neuroscience

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Gesture recognition is one of the important ways of human-computer interaction, which is mainly detected by visual technology. The temporal and spatial features are extracted by convolution of the video containing gesture. However, compared with the convolution calculation of a single image, multiframe image of dynamic gestures has more computation, more complex feature extraction, and more network parameters, which affects the recognition efficiency and real-time performance of the model. To solve above problems, a dynamic gesture recognition model based on CBAM-C3D is proposed. Key frame extraction technology, multimodal joint training, and network optimization with BN layer are used for making the network performance better. The experiments show that the recognition accuracy of the proposed 3D convolutional neural network combined with attention mechanism reaches 72.4% on EgoGesture dataset, which is improved greatly compared with the current main dynamic gesture recognition methods, and the effectiveness of the proposed algorithm is verified.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Dynamic Gesture Recognition Algorithm Based on 3D Convolutional Neural Network

Liu

Duan

et al. 2021

Computational Intelligence and Neuroscience

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“…Table 4 presents the intrasubject gesture recognition accuracy achieved by various methods on the first five subdatabases of NinaPro. Among these methods, the methods proposed in [ 13 , 36 , 37 ] are shallow learning frameworks, the methods proposed in [ 25 – 27 , 49 , 50 ] are single-view deep learning frameworks, and the method proposed in [ 31 ] is a multiview deep learning framework (i.e., MV-CNN). All the above-mentioned methods are non-end-to-end methods using engineered sEMG features as their input, and they used exactly the same intrasubject evaluation scheme as that was used in our work.…”

Section: Resultsmentioning

confidence: 99%

“…Experimental results in Table 4 demonstrate that when using all three views of multichannel sEMG as input, the proposed HVPN achieved the intrasubject gesture recognition accuracy of 88.4%, 85.8%, 68.2%, 72.9%, and 90.3% on NinaProDB1, DB2, DB3, DB4, and DB5, respectively, with the sliding window length of 200 ms, which outperformed not only shallow learning frameworks [ 13 , 36 , 37 ] but also deep learning frameworks [ 25 , 26 , 31 , 49 , 50 ] that were proposed for sEMG-based gesture recognition in recent years.…”

Section: Resultsmentioning

confidence: 99%

A Hierarchical View Pooling Network for Multichannel Surface Electromyography‐Based Gesture Recognition

Wei

Hong

2021

Computational Intelligence and Neuroscience

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Hand gesture recognition based on surface electromyography (sEMG) plays an important role in the field of biomedical and rehabilitation engineering. Recently, there is a remarkable progress in gesture recognition using high-density surface electromyography (HD-sEMG) recorded by sensor arrays. On the other hand, robust gesture recognition using multichannel sEMG recorded by sparsely placed sensors remains a major challenge. In the context of multiview deep learning, this paper presents a hierarchical view pooling network (HVPN) framework, which improves multichannel sEMG-based gesture recognition by learning not only view-specific deep features but also view-shared deep features from hierarchically pooled multiview feature spaces. Extensive intrasubject and intersubject evaluations were conducted on the large-scale noninvasive adaptive prosthetics (NinaPro) database to comprehensively evaluate our proposed HVPN framework. Results showed that when using 200 ms sliding windows to segment data, the proposed HVPN framework could achieve the intrasubject gesture recognition accuracy of 88.4%, 85.8%, 68.2%, 72.9%, and 90.3% and the intersubject gesture recognition accuracy of 84.9%, 82.0%, 65.6%, 70.2%, and 88.9% on the first five subdatabases of NinaPro, respectively, which outperformed the state-of-the-art methods.

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“…Object detection is one of the three basic tasks in the field of computer vision, 4 which is parallel to the other two basic tasks of image processing: image classification 5 and image semantic segmentation 6‐8 . Object detection refers to finding the target object in the input image.…”

Section: Related Workmentioning

confidence: 99%

Detection algorithm of safety helmet wearing based on deep learning

Huang

et al. 2021

Concurrency and Computation

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137

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In the production and construction of industry, safety accidents caused by unsafe behaviors of staff often occur. In a complex construction site scene, due to improper operations by personnel, huge safety risks will be buried in the entire production process. The use of deep learning algorithms to replace manual monitoring of site safety regulations is a powerful guarantee for sticking to the line of safety in production. First, the improved YOLO v3 algorithm is used to output the predicted anchor box of the target object, and then pixel feature statistics are performed on the anchor box, and the weight coefficients are respectively multiplied to output the confidence of the standard wearing of the helmet in each predicted anchor box area, according to the empirical threshold determine whether workers meet the standards for wearing helmets. Experimental results show that the helmet wearing detection algorithm based on deep learning in this paper increases the feature map scale, optimizes the prior dimensional algorithm of specific helmet dataset, and improves the loss function, and then combines image processing pixel feature statistics to accurately detect whether the helmet is worn by the standard. The final result is that mAP reaches 93.1% and FPS reaches 55 f/s. In the helmet recognition task, compared to the original YOLO v3 algorithm, mAP is increased by 3.5% and FPS is increased by 3 f/s. It shows that the improved detection algorithm has a better effect on the detection speed and accuracy of the helmet detection task.

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Gesture recognition based on surface electromyography‐featureimage

Cited by 82 publications

References 65 publications

[Retracted] Dynamic Gesture Recognition Algorithm Based on 3D Convolutional Neural Network

[Retracted] Dynamic Gesture Recognition Algorithm Based on 3D Convolutional Neural Network

A Hierarchical View Pooling Network for Multichannel Surface Electromyography‐Based Gesture Recognition

Detection algorithm of safety helmet wearing based on deep learning

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