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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 autonomous personalised feedback including verbal cues over virtual camera 3D replay and an end-of-session progress report. The prototype demonstrated autonomous assessment on future data based on learning from prior examples, aligned with skill level, flexible coaching scenarios and coaching rules. The generated intuitive diagnostic feedback consisted of elements of safety and performance for tennis swing technique, where each swing sample was comparedwith the expert. For safety aspects of the relative swing width, the prototype showed improved assessment (from 81% to 91%) when taking into account occluded parts of the pelvis. The next-generation of augmented coaching, personalised rehabilitation and exergaming systems, based on the concepts presented, will be able to help improve end-user's sport discipline-specific techniques by learning from small expert-labelled data sets, adapting and providing personalised intuitive autonomous assessment and diagnostic feedback aligned with a specified coaching programme and context requirements.

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