The University of Southampton
University of Southampton Institutional Repository

A machine learning multi-class approach for fall detection systems based on wearable sensors with a study on sampling rates selection

A machine learning multi-class approach for fall detection systems based on wearable sensors with a study on sampling rates selection
A machine learning multi-class approach for fall detection systems based on wearable sensors with a study on sampling rates selection

Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors’ sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection.

Data preprocessing, Fall detection, Feature extraction, Machine learning, Sampling rate, Wearable sensors
1424-8220
Zurbuchen, Nicolas
541c305b-2f7f-48e2-ac1c-238697ee1846
Wilde, Adriana
37ee0dec-a07f-4177-b291-96037fe48e14
Bruegger, Pascal
8b22f485-53b5-479b-b8ed-8721decaf649
Zurbuchen, Nicolas
541c305b-2f7f-48e2-ac1c-238697ee1846
Wilde, Adriana
37ee0dec-a07f-4177-b291-96037fe48e14
Bruegger, Pascal
8b22f485-53b5-479b-b8ed-8721decaf649

Zurbuchen, Nicolas, Wilde, Adriana and Bruegger, Pascal (2021) A machine learning multi-class approach for fall detection systems based on wearable sensors with a study on sampling rates selection. Sensors, 21 (3), [938]. (doi:10.3390/s21030938).

Record type: Article

Abstract

Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors’ sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection.

Text
sensors-1067322 - Version of Record
Available under License Creative Commons Attribution.
Download (4kB)

More information

Submitted date: 22 December 2020
Accepted/In Press date: 26 January 2021
e-pub ahead of print date: 30 January 2021
Published date: 1 February 2021
Additional Information: Funding Information: Funding: This research was funded by HES-SO University of Applied Sciences and Arts Western Switzerland. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: Data preprocessing, Fall detection, Feature extraction, Machine learning, Sampling rate, Wearable sensors

Identifiers

Local EPrints ID: 446740
URI: http://eprints.soton.ac.uk/id/eprint/446740
ISSN: 1424-8220
PURE UUID: 14696dc7-b8c5-413b-b1e3-575bb46fbeb7
ORCID for Adriana Wilde: ORCID iD orcid.org/0000-0002-1684-1539

Catalogue record

Date deposited: 19 Feb 2021 17:32
Last modified: 20 Feb 2021 02:46

Export record

Altmetrics

Contributors

Author: Nicolas Zurbuchen
Author: Adriana Wilde ORCID iD
Author: Pascal Bruegger

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×