Cross-cohort Evaluation of Machine Learning Approaches to Fall Detection from Accelerometer Data

Aneta Lisowska, Alison O'Neil, Ian Poole

2018

Abstract

Falls in seniors can lead to serious physical and psychological consequences. A fall detector can allow a fallen person to receive medical intervention promptly after the incident. The accelerometer data from smartphones or wearable devices can be used to detect falls without serious privacy intrusion. Common machine learning approaches to fall detection include supervised and novelty based methods. Previous studies have found that supervised methods have superior performance when tested on participants from the population cohort resembling the one they were trained on. In this study, we investigate if the performance remains superior when they are tested on a distinctly different population cohort. We train the supervised algorithms on data gathered using a wearable Silmee device (Cohort 1) and test on smartphone data from a publicly available data set (Cohort 2). We show that the performance of the supervised methods decreases when they are tested on distinctly different data, but that the decrease is not substantial. Novelty based fall detectors have better performance, suggesting that novelty based detectors might be better suited for real life applications.

Paper Citation

in Harvard Style

Lisowska A., O'Neil A. and Poole I. (2018). Cross-cohort Evaluation of Machine Learning Approaches to Fall Detection from Accelerometer Data. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF; ISBN 978-989-758-281-3, SciTePress, pages 77-82. DOI: 10.5220/0006554400770082

in Bibtex Style

@conference{healthinf18,
author={Aneta Lisowska and Alison O'Neil and Ian Poole},
title={Cross-cohort Evaluation of Machine Learning Approaches to Fall Detection from Accelerometer Data},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF},
year={2018},
pages={77-82},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006554400770082},
isbn={978-989-758-281-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF
TI - Cross-cohort Evaluation of Machine Learning Approaches to Fall Detection from Accelerometer Data
SN - 978-989-758-281-3
AU - Lisowska A.
AU - O'Neil A.
AU - Poole I.
PY - 2018
SP - 77
EP - 82
DO - 10.5220/0006554400770082
PB - SciTePress