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Authors: Aneta Lisowska 1 ; Alison O'Neil 2 and Ian Poole 2

Affiliations: 1 Toshiba Medical Visualization Systems Europe Ltd. and Heriot-Watt University, United Kingdom ; 2 Toshiba Medical Visualization Systems Europe Ltd., United Kingdom

ISBN: 978-989-758-281-3

Keyword(s): Fall Detection, Accelerometer Data, Machine Learning, Wearable Devices.

Related Ontology Subjects/Areas/Topics: Biomedical Engineering ; Biomedical Signal Processing ; Devices ; Distributed and Mobile Software Systems ; Health Engineering and Technology Applications ; Health Information Systems ; Human-Computer Interaction ; ICT, Ageing and Disability ; Mobile Technologies ; Mobile Technologies for Healthcare Applications ; Neural Rehabilitation ; Neurotechnology, Electronics and Informatics ; Pattern Recognition and Machine Learning ; Physiological Computing Systems ; Software Engineering ; Wearable Sensors and Systems

Abstract: Falls in seniors can lead to serious physical and psychological consequences. A fall detector can allow a fallen person to receive medical intervention promptly after the incident. The accelerometer data from smartphones or wearable devices can be used to detect falls without serious privacy intrusion. Common machine learning approaches to fall detection include supervised and novelty based methods. Previous studies have found that supervised methods have superior performance when tested on participants from the population cohort resembling the one they were trained on. In this study, we investigate if the performance remains superior when they are tested on a distinctly different population cohort. We train the supervised algorithms on data gathered using a wearable Silmee device (Cohort 1) and test on smartphone data from a publicly available data set (Cohort 2). We show that the performance of the supervised methods decreases when they are tested on distinctly different data, but t hat the decrease is not substantial. Novelty based fall detectors have better performance, suggesting that novelty based detectors might be better suited for real life applications. (More)

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Paper citation in several formats:
Lisowska, A.; O'Neil, A. and Poole, I. (2018). Cross-cohort Evaluation of Machine Learning Approaches to Fall Detection from Accelerometer Data.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-281-3, pages 77-82. DOI: 10.5220/0006554400770082

@conference{healthinf18,
author={Aneta Lisowska. and Alison O'Neil. and Ian Poole.},
title={Cross-cohort Evaluation of Machine Learning Approaches to Fall Detection from Accelerometer Data},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,},
year={2018},
pages={77-82},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006554400770082},
isbn={978-989-758-281-3},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,
TI - Cross-cohort Evaluation of Machine Learning Approaches to Fall Detection from Accelerometer Data
SN - 978-989-758-281-3
AU - Lisowska, A.
AU - O'Neil, A.
AU - Poole, I.
PY - 2018
SP - 77
EP - 82
DO - 10.5220/0006554400770082

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