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

Gascon, Alberto ; Akbarian, Fatemeh LU ; Aminifar, Amir LU orcid ; Marco, Alvaro and Casas, Roberto (2026)
Abstract
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.
Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
in press
subject
host publication
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
pages
6 pages
language
English
LU publication?
yes
id
a64e6e20-0494-4c5e-999e-c93b3cdd6aca
date added to LUP
2026-03-25 16:35:37
date last changed
2026-03-26 10:04:36
@inproceedings{a64e6e20-0494-4c5e-999e-c93b3cdd6aca,
  abstract     = {{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.}},
  author       = {{Gascon, Alberto and Akbarian, Fatemeh and Aminifar, Amir and Marco, Alvaro and Casas, Roberto}},
  booktitle    = {{European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning}},
  language     = {{eng}},
  title        = {{Lightweight personalisation for MEMS-based wearables : a padel stroke recognition case study}},
  year         = {{2026}},
}