Evaluating Assistive Technology Outcomes in Boccia Athletes with Disabilities Using AI-Based Kinematic Analysis.

Wann-Yun Shieh, Yan-Ying Ju, Shiu-Yuan Yang, I-Chun Chen, Hsin-Yi Kathy Cheng

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In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

Measuring Assistive Technology Outcomes via AI-Based Kinematic Modeling of Individualized Routine Learning in Elite Boccia Athletes with Severe Cerebral Palsy: A Longitudinal Case Series.Bioengineering (Basel, Switzerland) · 2026
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5 authors.

Wann-Yun ShiehDepartment of Computer Science and Information Engineering, College of Engineering, Chang Gung University, Taoyuan 333, Taiwan.ORCID 0000-0003-4325-3553

Yan-Ying JuDepartment of Adapted Physical Education, National Taiwan Sport University, Taoyuan 333, Taiwan.ORCID 0000-0002-1290-7676

Shiu-Yuan YangGraduate Institute of Early Intervention, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan.

I-Chun ChenGraduate Institute of Early Intervention, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan.

Hsin-Yi Kathy ChengDepartment of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan.ORCID 0000-0001-8855-1170

Funding

National science and technology council, Taiwan 107-2221-E-182-049-MY3National science and technology council, Taiwan NSTC 110-2221-E-179-001-MY3National science and technology council, Taiwan NSTC 110-2221-E-182-012-MY3National science and technology council, Taiwan NSTC 113-2221-E-182-020-MY3

6 · The paper itself

Abstract

This study explores how artificial intelligence (AI) can support the evaluation of assistive technology outcomes in adaptive sports, focusing on elite boccia athletes with disabilities. Using a multi-stage motion analysis framework, we integrated OpenPose, ViTPose, and Lifting to estimate seated joint kinematics with greater precision. Match footage from 12 athletes at the 2018 Asia-Pacific Boccia Open was analyzed across five biomechanical phases: preparation, acceleration, peak, release, and follow-through. AI-enhanced 2D and 3D pose estimation methods were applied to assess throwing strategies and motor variability. ViTPose outperformed OpenPose in joint detection accuracy (F1-score: 85% vs. 79.5%), while Lifting improved 3D estimation by reducing joint position error by 16%. Principal Component Analysis revealed greater movement consistency in overhand throws compared to underhand techniques. The proposed pipeline provides an interpretable and scalable method for measuring performance, motor control, and strategy-specific movement outcomes in boccia, offering practical applications for evidence-based coaching, athlete classification, and the design of inclusive assistive sport technologies.

Indexed as

adaptive sportsAI-based motion analysisassistive technologybocciainclusive sportpose estimation

Identifiers

PMID40722377

DOI10.3390/bioengineering12070684

PMCPMC12292824

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.