Semi-Supervised Action Quality Assessment With Self-Supervised Segment Feature Recovery

Zhang, Shaojie; Pan, Jiahui; Gao, Jibin; Zheng, Wei‐Shi

doi:10.1109/tcsvt.2022.3143549

Cited by 22 publications

(3 citation statements)

References 33 publications

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“…2) Assessment metrics: In line with established methods [41]- [43], we evaluate the regression performance using Pearson's correlation (ρ), mean absolute error (MAE), and root mean squared error (RMSE). Since different therapists may assign slightly different scores to individual rehabilitation movements, the evaluation process should focus on measuring the consistency between the model's predictions and the ground truth.…”

Section: Methodsmentioning

confidence: 99%

An Expert-Knowledge-Based Graph Convolutional Network for Skeleton- Based Physical Rehabilitation Exercises Assessment

He,

Chen,

Wang

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Physical therapists play a crucial role in guiding patients through effective and safe rehabilitation processes according to medical guidelines. However, due to the therapistpatient imbalance, it is neither economical nor feasible for therapists to provide guidance to every patient during recovery sessions. Automated assessment of physical rehabilitation can help with this problem, but accurately quantifying patients' training movements and providing meaningful feedback poses a challenge. In this paper, an Expert-knowledge-based Graph Convolutional approach is proposed to automate the assessment of the quality of physical rehabilitation exercises. This approach utilizes experts' knowledge to improve the spatial feature extraction ability of the Graph Convolutional module and a Gated pooling module for feature aggregation. Additionally, a Transformer module is employed to capture long-range temporal dependencies in the movements. The attention scores and weight matrix obtained through this approach can serve as interpretability tools to help therapists understand the assessment model and assist patients in improving their exercises. The effectiveness of the proposed method is verified on the KIMORE dataset, achieving state-ofthe-art performance compared to existing models. Experimental results also illustrate the interpretability of the method in both spatial and temporal dimensions.

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Section: Methodsmentioning

confidence: 99%

An Expert-Knowledge-Based Graph Convolutional Network for Skeleton- Based Physical Rehabilitation Exercises Assessment

He,

Chen,

Wang

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…For model design, Zhang [14] proposed to divide videos into different stages to learn the relationships between them. SJ Zhang [15] proposed a method that used Encoder/Decoder network to implement semi-supervised action quality assessment. Jain [16] proposed a new assess action quality method that transforming action quality assessment into comparing action videos with reference videos.…”

Section: Action Quality Assessmentmentioning

confidence: 99%

A novel blind action quality assessment based on multi-headed GRU network and attention mechanism

Sun

Zhang

et al. 2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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Objective action quality assessment (AQA) is a complex machine vision task because existing AQA assessment models can't effectively fit the subjective assessment. To address this issue, we propose a novel blind action quality assessment method. By processing the video data with spatial and temporal features, the performance of the model is effectively improved. In addition, we also proposed a new loss function to better train the model, which combines the information entropy of the data. Finally, the experimental results show that on the existing datasets AQA-7 and JIGSAWS are significantly improved, reaching 0.63 and 0.57, respectively.

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“…Most importantly, pose-only features do not take into account visual cues like splashes, which are crucial to judging. The state-of-the-art research focuses on vision-based methods [11], [12], [16], [17] as they can make full use of the visual features provided by image sequences and have achieved great success in recent years.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Graph Convolutional Networks for Action Quality Assessment

Zhou

Shum

et al. 2023

IEEE Trans. Circuits Syst. Video Technol.

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Action quality assessment (AQA) automatically evaluates how well humans perform actions in a given video, a technique widely used in fields such as rehabilitation medicine, athletic competitions, and specific skills assessment. However, existing works that uniformly divide the video sequence into small clips of equal length suffer from intra-clip confusion and inter-clip incoherence, hindering the further development of AQA. To address this issue, we propose a hierarchical graph convolutional network (GCN). First, semantic information confusion is corrected through clip refinement, generating the 'shot' as the basic action unit. We then construct a scene graph by combining several consecutive shots into meaningful scenes to capture local dynamics. These scenes can be viewed as different procedures of a given action, providing valuable assessment cues. The video-level representation is finally extracted via sequential action aggregation among scenes to regress the predicted score distribution, enhancing discriminative features and improving assessment performance. Experiments on the AQA-7, MTL-AQA, and JIGSAWS datasets demonstrate the superiority of the proposed hierarchical GCN over state-of-the-art methods.

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Semi-Supervised Action Quality Assessment With Self-Supervised Segment Feature Recovery

Cited by 22 publications

References 33 publications

An Expert-Knowledge-Based Graph Convolutional Network for Skeleton- Based Physical Rehabilitation Exercises Assessment

An Expert-Knowledge-Based Graph Convolutional Network for Skeleton- Based Physical Rehabilitation Exercises Assessment

A novel blind action quality assessment based on multi-headed GRU network and attention mechanism

Hierarchical Graph Convolutional Networks for Action Quality Assessment

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