Computational intelligence for qualitative coaching diagnostics: Automated assessment of tennis swings to improve performance and safety
Boris Ba\v{c}i\'c, Patria Hume

TL;DR
This paper presents a prototype system that uses computational intelligence and 3D tennis motion data to provide personalized qualitative feedback on tennis swings, aiming to enhance technique, safety, and performance.
Contribution
It introduces a novel integrated approach combining kinesiology and AI to deliver autonomous, personalized qualitative coaching feedback from multi-camera tennis swing data.
Findings
Prototype successfully assessed tennis swings with expert-level accuracy.
Adaptive assessment modules learned from data to improve feedback quality.
System provided safety and performance diagnostics aligned with skill levels.
Abstract
Coaching technology, wearables and exergames can provide quantitative feedback based on measured activity, but there is little evidence of qualitative feedback to aid technique improvement. To achieve personalised qualitative feedback, we demonstrated a proof-of-concept prototype combining kinesiology and computational intelligence that could help improving tennis swing technique utilising three-dimensional tennis motion data acquired from multi-camera video. Expert data labelling relied on virtual 3D stick figure replay. Diverse assessment criteria for novice to intermediate skill levels and configurable coaching scenarios matched with a variety of tennis swings (22 backhands and 21 forehands), included good technique and common errors. A set of selected coaching rules was transferred to adaptive assessment modules able to learn from data, evolve their internal structures and produce…
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