Fall Prediction Amongst the Elderly Using Data from an Ambient Assisted Living System

Philip Branch, Divya Sridharam, Andre Ferretto, Tim Carroll

2023

Abstract

Falls amongst the elderly are life threatening. Being able to predict falls means steps could be taken to reduce fall likelihood or severity. In this paper we report on our work using data generated by HalleyAssist, an advanced Ambient Assisted Living System, to predict falls amongst the elderly. HalleyAssist unobtrusively monitors older people using sensors to provide services to help them with their day-to-day activities. We conducted a three-month trial of the HalleyAssist system with six households of older people primarily to gauge acceptance and utility of the system. During the trial we also asked participants to keep a ’falls diary’ in which they recorded the date, time and location of any falls. After the initial trial we continued monitoring one of the participants (with her consent) who was susceptible to falls, for an additional seven months. Over the ten months of the trial she fell 32 times on 28 days. None of the other participants fell during the trial. We analysed data from the sensors and correlated it with whether she fell later in the day. Using techniques from machine learning we were able to identify features that enabled a fall to be predicted with 64.9 % accuracy.

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Paper Citation


in Harvard Style

Branch P., Sridharam D., Ferretto A. and Carroll T. (2023). Fall Prediction Amongst the Elderly Using Data from an Ambient Assisted Living System. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF; ISBN 978-989-758-631-6, SciTePress, pages 218-223. DOI: 10.5220/0011603400003414


in Bibtex Style

@conference{healthinf23,
author={Philip Branch and Divya Sridharam and Andre Ferretto and Tim Carroll},
title={Fall Prediction Amongst the Elderly Using Data from an Ambient Assisted Living System},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF},
year={2023},
pages={218-223},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011603400003414},
isbn={978-989-758-631-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF
TI - Fall Prediction Amongst the Elderly Using Data from an Ambient Assisted Living System
SN - 978-989-758-631-6
AU - Branch P.
AU - Sridharam D.
AU - Ferretto A.
AU - Carroll T.
PY - 2023
SP - 218
EP - 223
DO - 10.5220/0011603400003414
PB - SciTePress