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Article

A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems

1
Computer Engineering Department, University of Alcalá, 28801 Alcalá de Henares, Spain
2
Physics Department, University of Alcalá, 28801 Alcalá de Henares, Spain
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(11), 1831; https://doi.org/10.3390/electronics9111831
Received: 7 October 2020 / Revised: 26 October 2020 / Accepted: 29 October 2020 / Published: 3 November 2020
(This article belongs to the Special Issue AI and ML in the Future of Wearable Devices)
People’s life expectancy is increasing, resulting in a growing elderly population. That population is subject to dependency issues, falls being a problematic one due to the associated health complications. Some projects are trying to enhance the independence of elderly people by monitoring their status, typically by means of wearable devices. These devices often feature Machine Learning (ML) algorithms for fall detection using accelerometers. However, the software deployed often lacks reliable data for the models’ training. To overcome such an issue, we have developed a publicly available fall simulator capable of recreating accelerometer fall samples of two of the most common types of falls: syncope and forward. Those simulated samples are like real falls recorded using real accelerometers in order to use them later as input for ML applications. To validate our approach, we have used different classifiers over both simulated falls and data from two public datasets based on real data. Our tests show that the fall simulator achieves a high accuracy for generating accelerometer data from a fall, allowing to create larger datasets for training fall detection software in wearable devices. View Full-Text
Keywords: fall detection; machine learning; simulation; wearable devices fall detection; machine learning; simulation; wearable devices
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MDPI and ACS Style

Collado-Villaverde, A.; Cobos, M.; Muñoz, P.; F. Barrero, D. A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems. Electronics 2020, 9, 1831. https://doi.org/10.3390/electronics9111831

AMA Style

Collado-Villaverde A, Cobos M, Muñoz P, F. Barrero D. A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems. Electronics. 2020; 9(11):1831. https://doi.org/10.3390/electronics9111831

Chicago/Turabian Style

Collado-Villaverde, Armando, Mario Cobos, Pablo Muñoz, and David F. Barrero. 2020. "A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems" Electronics 9, no. 11: 1831. https://doi.org/10.3390/electronics9111831

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