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

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

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