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
Zurbuchen, Nicolas
541c305b-2f7f-48e2-ac1c-238697ee1846
Wilde, Adriana
4f9174fe-482a-4114-8e81-79b835946224
Bruegger, Pascal
8b22f485-53b5-479b-b8ed-8721decaf649
30 January 2021
Zurbuchen, Nicolas
541c305b-2f7f-48e2-ac1c-238697ee1846
Wilde, Adriana
4f9174fe-482a-4114-8e81-79b835946224
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).
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.
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sensors-1067322
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Submitted date: 22 December 2020
Accepted/In Press date: 26 January 2021
e-pub ahead of print date: 30 January 2021
Published date: 30 January 2021
Additional Information:
Funding Information:
This research was funded by HES-SO University of Applied Sciences and Arts Western Switzerland. Acknowledgments: The authors wish to express their gratitude to Juan Ye from the School of Computer Science at the University of St Andrews; Adam Prugel-Bennett and Jonathon Hare from the University of Southampton for their insightful comments on early stages of this work; the anonymous reviewers for their interesting and constructive comments.
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.
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
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Date deposited: 19 Feb 2021 17:32
Last modified: 12 Nov 2024 02:46
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Contributors
Author:
Nicolas Zurbuchen
Author:
Adriana Wilde
Author:
Pascal Bruegger
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