A Sparse Representation Classification for Noise Robust Wrist-based Fall Detection

Farah Othmen, Farah Othmen, Farah Othmen, André Eugenio Lazzaretti, Mouna Baklouti, Marwa Jmal, Mohamed Abid

2021

Abstract

Elderly falls are becoming a more crucial and major health problem relatively with the significant growth of the involved population over the years. Wrist-based fall detection solution gained much interest for its comfortable and indoor-outdoor use, yet, a very moving and unstable location to the Inertial measurement unit. Indeed, acquired data might be exposed to random noises challenging the classifier’s reliability to spot falls among other daily activities. In this paper, we address the limits faced by Machine Learning models regarding noisy and overlapped data by proposing a study of the Supervised Dictionary Learning (SDL) technique for on-wrist fall detection. Following the same prior work experimental protocol, the five most popular SDL models were evaluated and compared in performance with two benchmark Machine learning models. The evaluation setup follows two main experiments; processing clean data and casting different additive white Gaussian noise (AWGN). A distinguishable achievement was obtained by the SDL algorithms, of which the Sparse Representation-based Classifier (SRC) algorithm surpass other models especially using noisy data. The latter maintained almost 98% for 0db AWGN versus 96.4% for KNN.

Paper Citation

in Harvard Style

Othmen F., Lazzaretti A., Baklouti M., Jmal M. and Abid M. (2021). A Sparse Representation Classification for Noise Robust Wrist-based Fall Detection. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF; ISBN 978-989-758-490-9, SciTePress, pages 409-416. DOI: 10.5220/0010238804090416

in Bibtex Style

@conference{healthinf21,
author={Farah Othmen and André Eugenio Lazzaretti and Mouna Baklouti and Marwa Jmal and Mohamed Abid},
title={A Sparse Representation Classification for Noise Robust Wrist-based Fall Detection},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF},
year={2021},
pages={409-416},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010238804090416},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF
TI - A Sparse Representation Classification for Noise Robust Wrist-based Fall Detection
SN - 978-989-758-490-9
AU - Othmen F.
AU - Lazzaretti A.
AU - Baklouti M.
AU - Jmal M.
AU - Abid M.
PY - 2021
SP - 409
EP - 416
DO - 10.5220/0010238804090416
PB - SciTePress