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January 1, 2026International Journal of Business Intelligence and Data MiningOpen Access

Real-time lightweight pose estimation for sports motion analysis on mobile and edge platforms

Haibo Wang Ningning Li Chao Liu Bin Wu

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Key Points

This research aims to develop and validate a lightweight pose estimation algorithm for analyzing sports motion in real-time.
Developed a lightweight pose estimation algorithm suitable for mobile and edge computing platforms.
Conducted performance evaluations under various conditions to test real-time processing ability.
Implemented tests within sports scenarios to gather quantitative analysis data.
Achieved an accuracy of 92% in real-time pose estimation in sports applications.
Processing time optimized to under 50ms per frame on mobile devices.
User feedback indicates improved analysis and insights during sports training sessions.

Implication

Demonstrates effective pose estimation in sports, highlighting potential for mobile use.

Abstract

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Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/6a0ff33bd674f7c03778bbd0 https://doi.org/10.1504/ijbidm.2026.10078556

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1Visual depth models coupled with 3D pose estimation for sports body training evaluationOriginal Research · 2026
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3AI-driven communication networks for real-time sports analytics and fan engagement in edge-IoT environmentsOriginal Research · 2026
4Deep learning-based football training movement analysis and penalty feedback researchOriginal Research · 2026
5Dynamic scene motion target segmentation method for physical educationOriginal Research · 2026

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