Taekwondo Action Recognition Method Based on Partial Perception Structure Graph Convolution Framework

Liang, Jianqiao; Zuo, Guocai

doi:10.1155/2022/1838468

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Through this operation, the defects of manual design features are avoided, and the spatial features on the time series are obtained. [ 45 ].…”

Section: Methodsmentioning

confidence: 99%

Spatial-Temporal Graph Convolutional Framework for Yoga Action Recognition and Grading

Wang

2022

Computational Intelligence and Neuroscience

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The rapid development of the Internet has changed our lives. Many people gradually like online video yoga teaching. However, yoga beginners cannot master the standard yoga poses just by learning through videos, and high yoga poses can bring great damage or even disability to the body if they are not standard. To address this problem, we propose a yoga action recognition and grading system based on spatial-temporal graph convolutional neural network. Firstly, we capture yoga movement data using a depth camera. Then we label the yoga exercise videos frame by frame using long short-term memory network; then we extract the skeletal joint point features sequentially using graph convolution; then we arrange each video frame from spatial-temporal dimension and correlate the joint points in each frame and neighboring frames with spatial-temporal information to obtain the connection between joints. Finally, the identified yoga movements are predicted and graded. Experiment proves that our method can accurately identify and classify yoga poses; it also can identify whether yoga poses are standard or not and give feedback to yogis in time to prevent body damage caused by nonstandard poses.

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“…Through this operation, the defects of manual design features are avoided, and the spatial features on the time series are obtained. [ 45 ].…”

Section: Methodsmentioning

confidence: 99%

Spatial-Temporal Graph Convolutional Framework for Yoga Action Recognition and Grading

Wang

2022

Computational Intelligence and Neuroscience

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“…The paper by Liang & Zuo (2022) proved that movement classifications from videos and mapping them to score a Taekwondo sparring (fighting) match are also possible. They introduced a graph convolution framework to recognize, segment, and evaluate Taekwondo actions with a specific part of the perception structure.…”

Section: Usagementioning

confidence: 99%

Evaluation of Taekwondo Poomsae movements using skeleton points

Fernando,

Sandaruwan,

Athapaththu

2024

J. Natn. Sci. Foundation Sri Lanka

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Taekwondo is a widely practised martial art and an Olympic sport. In Taekwondo, Poomsae movements are essential, as they form the foundation of the sport and are fundamental for success in competitions. The evaluation of Poomsae movements in Taekwondo has been a subjective process, relying heavily on human judgments. This study addresses the above issue by developing a systematic approach to evaluate Poomsae movements using computer vision. A long short-term memory-based (LSTM-based) machine learning (ML) model was developed and evaluated for its effectiveness in Poomsae movement evaluation. The study also aimed to develop this model as an assistant for self-evaluation, that enables Taekwondo players to enhance their skills at their own pace. For this study, a dataset was created specially by recording Poomsae movements of Taekwondo players from the University of Colombo. The technical infrastructure used to capture skeleton point data was cost-effective and easily replicable in other settings. Small video clips containing Taekwondo movements were recorded using a mobile phone camera and the skeleton point data was extracted using the MediaPipe Python library. The model was able to achieve 61% of accuracy when compared with the domain experts’ results. Overall, the study successfully achieved its objectives of defining a self-paced approach to evaluate Poomsae while overcoming human subjectivity otherwise unavoidable in manual evaluation processes. The feedback of domain experts was also considered to finetune the model for better performance.

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“…Kong et al [ 15 ] proposed an automatic analysis framework for broadcasted Taekwondo videos, integrating a structure-preserving object tracker with a principal component analysis (PCA) network. Liang et al [ 16 ] explored a novel evaluation method for Taekwondo competitions combining long short-term memory (LSTM) with a spatial temporal graph convolutional network (ST-GCN). Recently, Lee et al [ 17 ] reported over 80% recognition accuracy using a convolutional neural network (CNN)-based model that processes a sequence of key-frame images to identify basic Taekwondo unit actions.…”

Section: Introductionmentioning

confidence: 99%

Action Recognition of Taekwondo Unit Actions Using Action Images Constructed with Time-Warped Motion Profiles

Lim,

Luo,

Lee

et al. 2024

Sensors

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Taekwondo has evolved from a traditional martial art into an official Olympic sport. This study introduces a novel action recognition model tailored for Taekwondo unit actions, utilizing joint-motion data acquired via wearable inertial measurement unit (IMU) sensors. The utilization of IMU sensor-measured motion data facilitates the capture of the intricate and rapid movements characteristic of Taekwondo techniques. The model, underpinned by a conventional convolutional neural network (CNN)-based image classification framework, synthesizes action images to represent individual Taekwondo unit actions. These action images are generated by mapping joint-motion profiles onto the RGB color space, thus encapsulating the motion dynamics of a single unit action within a solitary image. To further refine the representation of rapid movements within these images, a time-warping technique was applied, adjusting motion profiles in relation to the velocity of the action. The effectiveness of the proposed model was assessed using a dataset compiled from 40 Taekwondo experts, yielding remarkable outcomes: an accuracy of 0.998, a precision of 0.983, a recall of 0.982, and an F1 score of 0.982. These results underscore this time-warping technique’s contribution to enhancing feature representation, as well as the proposed method’s scalability and effectiveness in recognizing Taekwondo unit actions.

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Taekwondo Action Recognition Method Based on Partial Perception Structure Graph Convolution Framework

Cited by 5 publications

References 21 publications

Spatial-Temporal Graph Convolutional Framework for Yoga Action Recognition and Grading

Spatial-Temporal Graph Convolutional Framework for Yoga Action Recognition and Grading

Evaluation of Taekwondo Poomsae movements using skeleton points

Action Recognition of Taekwondo Unit Actions Using Action Images Constructed with Time-Warped Motion Profiles

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