A comparison of machine learning algorithms for fall detection using wearable sensors
A comparison of machine learning algorithms for fall detection using wearable sensors
The proportion of people 60 years old and above is expected to double globally to reach 22% by 2050. This creates societal challenges such as the increase of age-related illnesses and the need for caregivers. Falls are a major threat for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance.
This paper presents the development of a Fall Detection System (FDS) using an accelerometer combined with a gyroscope worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We compared five Machine Learning algorithms. We first applied 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%.
data preprocessing, fall detection, feature extraction, machine learning, sampling rate, wearable sensors
427-431
Zurbuchen, Nicolas
541c305b-2f7f-48e2-ac1c-238697ee1846
Bruegger, Pascal
29312df1-ea70-4abe-9cea-560ef04c692c
Wilde, Adriana
4f9174fe-482a-4114-8e81-79b835946224
21 February 2020
Zurbuchen, Nicolas
541c305b-2f7f-48e2-ac1c-238697ee1846
Bruegger, Pascal
29312df1-ea70-4abe-9cea-560ef04c692c
Wilde, Adriana
4f9174fe-482a-4114-8e81-79b835946224
Zurbuchen, Nicolas, Bruegger, Pascal and Wilde, Adriana
(2020)
A comparison of machine learning algorithms for fall detection using wearable sensors.
In 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020.
IEEE.
.
(doi:10.1109/ICAIIC48513.2020.9065205).
Record type:
Conference or Workshop Item
(Paper)
Abstract
The proportion of people 60 years old and above is expected to double globally to reach 22% by 2050. This creates societal challenges such as the increase of age-related illnesses and the need for caregivers. Falls are a major threat for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance.
This paper presents the development of a Fall Detection System (FDS) using an accelerometer combined with a gyroscope worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We compared five Machine Learning algorithms. We first applied 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%.
Text
comparison-machine-learning-algorithms-fall-detection-using-wearable-sensors
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Accepted/In Press date: 27 December 2019
Published date: 21 February 2020
Venue - Dates:
IEEE International Conference on Artificial Intelligence in Information and Communication, Takakura Hotel Fukuoka, Fukuoka, Japan, 2020-02-19 - 2020-02-21
Keywords:
data preprocessing, fall detection, feature extraction, machine learning, sampling rate, wearable sensors
Identifiers
Local EPrints ID: 437602
URI: http://eprints.soton.ac.uk/id/eprint/437602
PURE UUID: f43e9baf-5e56-475d-873d-a1e8aad2f9c2
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Date deposited: 06 Feb 2020 17:32
Last modified: 17 Mar 2024 03:23
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Contributors
Author:
Nicolas Zurbuchen
Author:
Pascal Bruegger
Author:
Adriana Wilde
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