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Exercise quality assessment in monocular video streaming.

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Title: Exercise quality assessment in monocular video streaming.
Authors: Zhang, Yongchang1 (AUTHOR), Xu, Boxuan1 (AUTHOR), Lin, Zhaowen1 (AUTHOR) linzw@bupt.edu.cn, Li, Junjie1 (AUTHOR), Ming, Anlong1 (AUTHOR) mal@bupt.edu.cn
Source: Engineering Applications of Artificial Intelligence. Aug2026:Part 2, Vol. 177, pN.PAG-N.PAG. 1p.
Subjects: Pose estimation (Computer vision), Streaming media, Home rehabilitation, Time series analysis, Physical activity, Artificial intelligence, Psychological feedback
Abstract: The recent proliferation of home-based exercise content has garnered significant attention. This has led to an increasing demand for Artificial Intelligence (AI) devices capable of automatically assessing exercise quality and providing guidance. However, existing real-time exercise quality assessment algorithms require instructors and learners to share similar camera views. Furthermore, these methods often rely on pre-labeled data, support a limited number of exercise actions, and offer restricted feedback. Among the numerous videos where data is hard to pre-label, instructors and learners may have unrestricted camera views and inconsistent body shape, while instructors may demonstrate unpredicted actions. To address the aforementioned challenges, we propose a method for Exercise Quality Assessment in Monocular Videos (MV-EQA), which incorporates a Skeleton Mapping and View Aligning (SMVA) module, a Multi-Feature Dynamic Time Warping (MF-DTW) module, and online/offline Exercise Quality Assessment (EQA) modules. Specifically, SMVA utilizes a lightweight encoder–decoder network based on transformer architecture that effectively handles differences in view and skeleton between learners and instructors while preserving inherent variations in their movements; MF-DTW utilizes multiple body information for temporal alignment; online/offline EQA modules enable online feedback (scoring with visual comparison) and offline feedback (reviews with comments). Extensive experiments indicate the superiority of our approach over other methods in EQA tasks. The code is available at link. • Reveals view and skeleton inconsistency impacts on home exercise assessment. • SMVA method bypasses 2D-to-3D errors and handles view and skeleton differences. • Introduces MF-DTW for precise temporal alignment between learner and instructor. • Uses large language models to provide detailed exercise quality feedback. • Provides real-time multimodal scoring and visual comparison for exercises. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:The recent proliferation of home-based exercise content has garnered significant attention. This has led to an increasing demand for Artificial Intelligence (AI) devices capable of automatically assessing exercise quality and providing guidance. However, existing real-time exercise quality assessment algorithms require instructors and learners to share similar camera views. Furthermore, these methods often rely on pre-labeled data, support a limited number of exercise actions, and offer restricted feedback. Among the numerous videos where data is hard to pre-label, instructors and learners may have unrestricted camera views and inconsistent body shape, while instructors may demonstrate unpredicted actions. To address the aforementioned challenges, we propose a method for Exercise Quality Assessment in Monocular Videos (MV-EQA), which incorporates a Skeleton Mapping and View Aligning (SMVA) module, a Multi-Feature Dynamic Time Warping (MF-DTW) module, and online/offline Exercise Quality Assessment (EQA) modules. Specifically, SMVA utilizes a lightweight encoder–decoder network based on transformer architecture that effectively handles differences in view and skeleton between learners and instructors while preserving inherent variations in their movements; MF-DTW utilizes multiple body information for temporal alignment; online/offline EQA modules enable online feedback (scoring with visual comparison) and offline feedback (reviews with comments). Extensive experiments indicate the superiority of our approach over other methods in EQA tasks. The code is available at link. • Reveals view and skeleton inconsistency impacts on home exercise assessment. • SMVA method bypasses 2D-to-3D errors and handles view and skeleton differences. • Introduces MF-DTW for precise temporal alignment between learner and instructor. • Uses large language models to provide detailed exercise quality feedback. • Provides real-time multimodal scoring and visual comparison for exercises. [ABSTRACT FROM AUTHOR]
ISSN:09521976
DOI:10.1016/j.engappai.2026.114905