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Coaching technology, wearables and exergames can provide quantitativefeedback based on measured activity, but there is little evidence ofqualitative feedback to aid technique improvement. To achieve personalisedqualitative feedback, we demonstrated a proof-of-concept prototype combiningkinesiology and computational intelligence that could help improving tennisswing technique utilising three-dimensional tennis motion data acquired frommulti-camera video. Expert data labelling relied on virtual 3D stick figurereplay. Diverse assessment criteria for novice to intermediate skill levels andconfigurable coaching scenarios matched with a variety of tennis swings (22backhands and 21 forehands), included good technique and common errors. A setof selected coaching rules was transferred to adaptive assessment modules ableto learn from data, evolve their internal structures and produce autonomouspersonalised feedback including verbal cues over virtual camera 3D replay andan end-of-session progress report. The prototype demonstrated autonomousassessment on future data based on learning from prior examples, aligned withskill level, flexible coaching scenarios and coaching rules. The generatedintuitive diagnostic feedback consisted of elements of safety and performancefor tennis swing technique, where each swing sample was compared with theexpert. For safety aspects of the relative swing width, the prototype showedimproved assessment ...

Computational intelligence for qualitative coaching diagnostics: Automated assessment of tennis swings to improve performance and safety