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A comparison of machine learning algorithms for fall detection using wearable sensors

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%.
fall detection, Wearable sensors, Sampling rates, Machine Learning, data preprocessing, Feature extraction
IEEE Xplore
Zurbuchen, Nicolas
541c305b-2f7f-48e2-ac1c-238697ee1846
Bruegger, Pascal
29312df1-ea70-4abe-9cea-560ef04c692c
Wilde, Adriana Gabriela
37ee0dec-a07f-4177-b291-96037fe48e14
Zurbuchen, Nicolas
541c305b-2f7f-48e2-ac1c-238697ee1846
Bruegger, Pascal
29312df1-ea70-4abe-9cea-560ef04c692c
Wilde, Adriana Gabriela
37ee0dec-a07f-4177-b291-96037fe48e14

Zurbuchen, Nicolas, Bruegger, Pascal and Wilde, Adriana Gabriela (2020) A comparison of machine learning algorithms for fall detection using wearable sensors. In Proceedings of the IEEE 2nd International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2020). IEEE Xplore. 5 pp .

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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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, Japan, 2020-02-19 - 2020-02-21
Keywords: fall detection, Wearable sensors, Sampling rates, Machine Learning, data preprocessing, Feature extraction

Identifiers

Local EPrints ID: 437602
URI: http://eprints.soton.ac.uk/id/eprint/437602
PURE UUID: f43e9baf-5e56-475d-873d-a1e8aad2f9c2
ORCID for Adriana Gabriela Wilde: ORCID iD orcid.org/0000-0002-1684-1539

Catalogue record

Date deposited: 06 Feb 2020 17:32
Last modified: 07 Oct 2020 02:09

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