ViT-LLMR: Vision Transformer-based lower limb motion recognition from fusion signals of MMG and IMU

Zhang, Hanyang; Cao, Gangsheng; Xia, Chunming

doi:10.1016/j.bspc.2022.104508

Cited by 15 publications

(6 citation statements)

References 45 publications

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“…Yang et al 24 introduced the flexible strain sensor used in handwriting recognition based on machine learning, which highlighted the application potential of machine learning in realizing high-precision handwriting recognition. Zhang et al 25 proposed a lower limb action recognition method based on Vision Transformer, which highlighted the innovation of deep learning algorithm in action behavior recognition based on sensor data.…”

Section: Literature Reviewmentioning

confidence: 99%

Boxing behavior recognition based on artificial intelligence convolutional neural network with sports psychology assistant

Kong,

Duan

2024

Sci Rep

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The purpose of this study is to deeply understand the psychological state of boxers before the competition, and explore an efficient boxing action classification and recognition model supported by artificial intelligence (AI) technology through these psychological characteristics. Firstly, this study systematically measures the key psychological dimensions of boxers, such as anxiety level, self-confidence, team identity, and opponent attitude, through psychological scale survey to obtain detailed psychological data. Then, based on these data, this study innovatively constructs a boxing action classification and recognition model based on BERT fusion 3D-ResNet, which not only comprehensively considers psychological information, but also carefully considers action characteristics to improve the classification accuracy of boxing actions. The performance evaluation shows that the model proposed in this study is significantly superior to the traditional model in terms of loss value, accuracy and F1 value, and the accuracy reaches 96.86%. Therefore, through the comprehensive application of psychology and deep learning, this study successfully constructs a boxing action classification and recognition model that can fully understand the psychological state of boxers, which provides strong support for the psychological training and action classification of boxers.

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Section: Literature Reviewmentioning

confidence: 99%

Boxing behavior recognition based on artificial intelligence convolutional neural network with sports psychology assistant

Kong,

Duan

2024

Sci Rep

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“…This signal characteristic ensures that it can be stabilized within the time-frequency range of the same type of muscle movement through strict control. The DTW algorithm can be used to achieve the direct identification and analysis of these discrete sEMG signal sequences in the human body, avoiding the problem of failing to identify the signal sequences due to the inconsistency of the length and intensity of the feature sequences at a certain stage, and greatly improving the overall accuracy of the recognition and detection of EMG signals [25,26]. Therefore, in this study, the DTW algorithm is used to realize the automatic identification of feature sequences and generate accurate signal value sequences [26].…”

Section: Dtw Algorithm Implementationmentioning

confidence: 99%

“…overall accuracy of the recognition and detection of EMG signals [25,26]. Therefore, in this study, the DTW algorithm is used to realize the automatic identification of feature sequences and generate accurate signal value sequences [26].…”

Section: Dtw Algorithm Implementationmentioning

confidence: 99%

Hardware and Software Design and Implementation of Surface-EMG-Based Gesture Recognition and Control System

Zhang,

Han,

Huang

et al. 2024

Electronics

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The continuous advancement of electronic technology has led to the gradual integration of automated intelligent devices into various aspects of human life. Motion gesture-based human–computer interaction systems offer abundant information, user-friendly functionalities, and visual cues. Surface electromyography (sEMG) signals enable the decoding of muscle movements, facilitating the realization of corresponding control functions. Considering the inherent instability and minuscule nature of sEMG signals, this thesis proposes the integration of a dynamic time regularization algorithm to enhance gesture recognition detection accuracy and real-time system performance. The application of the dynamic time warping algorithm allows the fusion of three sEMG signals, enabling for the calculation of similarity between the sample and the model. This process facilitates gesture recognition and ensures effective communication between individuals and the 3D printed prosthesis. Utilizing this algorithm, the best feature model was generated by amalgamating six types of gesture classification model. A total of 600 training and evaluation experiments were performed, with each movement recognized 100 times. The experimental tests demonstrate that the accuracy of gesture recognition and prosthetic limb control using the temporal dynamic regularization algorithm achieves an impressive 93.75%, surpassing the performance of the traditional threshold control switch.

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“…where e NBB can be calculated using Equation (4), and e BB is the noise bandwidth. From Equation (3), it can be seen that the operational amplifier voltage noise e NV is mainly related to flicker noise e NBB and equivalent input noise voltage, both of which are related to the reciprocal of the frequency f .…”

Section: Design Of Mmg Signal Conditioning Circuitmentioning

confidence: 99%

“…The action potential is generated by MU and then transmitted through the activation of Na and K ions on the muscle fibers [3], thus forming EMG. The EMG signal on the skin surface is called the surface electromyography (sEMG) signal [4]. Mechanomyography (MMG) is mechanical vibration induced by fiber contraction, which is caused by the release of calcium ions by the sarcoplasmic reticulum in the muscle fibers in response to an action potential [2,5,6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Mechanomyography (MMG) Sensor Based on Piezo-Resistance Principle and with a Pyramidic Microarray

Fang,

Cao,

Qin

et al. 2023

Micromachines

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Flexible piezoresistive sensors built by printing nanoparticles onto soft substrates are crucial for continuous health monitoring and wearable devices. In this study, a mechanomyography (MMG) sensor was developed using a flexible piezoresistive MMG signal sensor based on a pyramidal polydimethylsiloxane (PDMS) microarray sprayed with carbon nanotubes (CNTs). The experiment was conducted, and the results show that the sensitivity of the sensor can reach 0.4 kPa−1 in the measurement range of 0~1.5 kPa, and the correlation reached 96%. This has further implications for the possibility that muscle activation can be converted into mechanical movement. The integrity of the sensor in terms of its MMG signal acquisition was tested based on five subjects who were performing arm bending and arm extending movements. The results of this test were promising.

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ViT-LLMR: Vision Transformer-based lower limb motion recognition from fusion signals of MMG and IMU

Cited by 15 publications

References 45 publications

Boxing behavior recognition based on artificial intelligence convolutional neural network with sports psychology assistant

Boxing behavior recognition based on artificial intelligence convolutional neural network with sports psychology assistant

Hardware and Software Design and Implementation of Surface-EMG-Based Gesture Recognition and Control System

A Novel Mechanomyography (MMG) Sensor Based on Piezo-Resistance Principle and with a Pyramidic Microarray

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