Lightweight personalisation for MEMS-based wearables : a padel stroke recognition case study

This work investigates lightweight personalisation of micro-electro-mechanical systems (MEMS)-based wearables using a public padel database as a case study. We compare a centralised CNN model, single-user models and two fine-tuning schemes (full and last-layer) on wrist-worn IMU data from 23 players and 13 stroke classes. Personalised models with data augmentation achieve weighted F1-scores above 90%, closing most of the gap to an optimistic single-user upper bound while reducing inter-subject variability. FLOP and memory analyses show that last-layer fine-tuning offers a favorable trade-off between accuracy and efficiency for on-device deployment in MEMS-based wearables.