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
Institute of Electrical and Electronics Engineers Inc.
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.
Institute of Electrical and Electronics Engineers Inc.
.
(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%.
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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: 09 Jan 2022 06:26
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Contributors
Author:
Nicolas Zurbuchen
Author:
Pascal Bruegger
Author:
Adriana Wilde
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