Effects of Sampling Rate and Window Length on Motion Recognition Using sEMG Armband Module

Kim, Tae-Hee; Kim, Jong Man; Koo, Bummo; Jung, Haneul; Nam, Yejin; Chang, Yunhee; Park, Sehoon; Kim, Young‐Ho

doi:10.1007/s12541-021-00546-6

Cited by 7 publications

(9 citation statements)

References 34 publications

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“…Hand motion recognition uses artificial neural networks, to support vector machines, decision trees, and k-nearest neighbor classifiers. The results show that the hand action classification accuracy increases with the sampling rate and window length [5]. Gao et al develops and proposes a new encoding scheme based on a review of previous approaches using the dynamic map concept for human motion recognition in RGB-D over different modalities (depth, skeleton, or RGB-D data).…”

Section: Related Workmentioning

confidence: 99%

Recognition of Hitting Action in Cyclic Anaerobic Volleyball by Acute Cooling Based on Improved Spatiotemporal Graph Convolutional Network

Jin-wen

Shen

2022

Wireless Communications and Mobile Computing

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Action recognition is the basis of intelligent sports training and has a unique role in improving athletes’ sports training ability. Traditional motion recognition cannot accurately identify changes in human skeletal motion, and the generalization ability is weak, which cannot meet the needs of modern sports. This paper is aimed at studying a new method of human motion recognition and better realizing the intelligent development of the Institute of Physical Education. This paper proposes an improved recognition mode of spatiotemporal graph convolutional network, builds a human skeleton feature recognition model, learns the changes of human skeleton motion, and masters the laws of human skeleton motion changes. This paper comprehensively compares the differences of action recognition of several different algorithms and proves the superiority of this method. Afterwards, a comprehensive comparison of 2D and 3D information data and a comparison of whole body and local action recognition methods were conducted to investigate the experience of athletes and trainers. The experimental results of this paper show that the spatiotemporal graph convolutional network can better realize the recognition of motion features, improve the sports training ability of athletes by 20%, and promote the development of sports.

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Section: Related Workmentioning

confidence: 99%

Recognition of Hitting Action in Cyclic Anaerobic Volleyball by Acute Cooling Based on Improved Spatiotemporal Graph Convolutional Network

Jin-wen

Shen

2022

Wireless Communications and Mobile Computing

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“…The impact of different environments on person recognition is shown in Fig. 2 [12][13]. In wrestling, these influencing factors exist widely, so a more in-depth calculation method should be used for their identification.…”

Section: Wrestling and Deep Learningmentioning

confidence: 99%

“…This paper adopts a human body identification method based on bone positioning. The first branch takes the overall skeleton point sequence [24,25,12,11,10,9,21,5,6,8,7,8,22,23,4,3,21,2,1,17,18,19,20,13,14,15,16] are input to a two-layer LSTM network, and the second layer of LSTM extracts the entire frame information. The second branch divides the body skeleton point sequence into the left branch [24,25,12,11,10,9,17,18,19,20], the torso [1,2,3,21,4] and the right branch [22,23,8,7,6,5,13,14,15,…”

Section: Wrestling and Deep Learningmentioning

confidence: 99%

“…The double-layer LSTM network is input, and the second layer of LSTM accepts the output of the first layer and processes it, making the judgment of actions more accurate. The third branch divides the body skeleton point sequence into the left arm [5,6,7,8,22,23], the right arm [9,10,11,12,24,25], the left leg [13,14,15,16], right leg [17,18,19,20] and torso [1,2,3,21,4], this branch temporal model is similar to the local temporal model to divide the human skeleton into more parts in order to better identify local detailed actions. It can effectively improve the accuracy of detailed action recognition.…”

Section: Wrestling and Deep Learningmentioning

confidence: 99%

“…c) The output layer is calculated through the loss function . d) For l=L-1 to 2, backpropagation is calculated according to the current layer type: If it is currently a fully connected layer: (12) If it is currently a fully convolutional layer: (13) If the current full pooling layer: (14) For l = 2 to L, update the first layer according to the type of the current layer , : If it is currently a fully connected layer: (15) (16) If it is currently a convolutional layer, for each convolution kernel there are: (17) (18) Iterating will stop when all parameter changes are less than .…”

Section: Lstm Neural Network Bone Localization Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

A Deep Learning Algorithm for Fusing Action Recognition and Psychological Characteristics of Wrestlers

2023

KSII TIIS

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Wrestling is one of the popular events for modern sports. It is difficult to quantitatively describe a wrestling game between athletes. And deep learning can help wrestling training by human recognition techniques. Based on the characteristics of latest wrestling competition rules and human recognition technologies, a set of wrestling competition video analysis and retrieval system is proposed. This system uses a combination of literature method, observation method, interview method and mathematical statistics to conduct statistics, analysis, research and discussion on the application of technology. Combined the system application in targeted movement technology. A deep learning-based facial recognition psychological feature analysis method for the training and competition of classical wrestling after the implementation of the new rules is proposed. The experimental results of this paper showed that the proportion of natural emotions of male and female wrestlers was about 50%, indicating that the wrestler's mentality was relatively stable before the intense physical confrontation, and the test of the system also proved the stability of the system.

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Deep Convolution Neural Network to Improve Hand Motion Classification Performance Against Varying Orientation Using Electromyography Signal

Triwiyanto,

Abdullayev,

Ahmed

2024

Int. J. Precis. Eng. Manuf.

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Effects of Sampling Rate and Window Length on Motion Recognition Using sEMG Armband Module

Cited by 7 publications

References 34 publications

Recognition of Hitting Action in Cyclic Anaerobic Volleyball by Acute Cooling Based on Improved Spatiotemporal Graph Convolutional Network

Recognition of Hitting Action in Cyclic Anaerobic Volleyball by Acute Cooling Based on Improved Spatiotemporal Graph Convolutional Network

A Deep Learning Algorithm for Fusing Action Recognition and Psychological Characteristics of Wrestlers

Deep Convolution Neural Network to Improve Hand Motion Classification Performance Against Varying Orientation Using Electromyography Signal

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