Fall Risk Assessment Using Wearable-Based Turn Detection: Comparison of Different Algorithms During Real-World Monitoring

Jose Albites-Sanabria, Jose Albites-Sanabria, Pierpaolo Palumbo, Stefania Bandinelli, Luca Palmerini, Luca Palmerini, Lorenzo Chiari, Lorenzo Chiari

2023

Abstract

Turning deficits have been linked to aging and movement disorders and are a common cause of falls and fractures. Despite previous works on the automatic identification of turns and on its relation to fall risk, different algorithms for turn identification have been used, but their agreement and differences have not been investigated. In this study, we compared the two most-used turn-validated algorithms (El-Gohary and Pham) using a dataset comprising real-world data from 171 community-dwelling older adults monitored for one week with a single wearable sensor. The quantity and quality of turn parameters were calculated and used as predictors of future falls. After the analysis, the El-Gohary and Pham algorithms identified 1,063,810 and 942,845 turns, respectively. The agreement of the algorithms showed a very high to moderate correlation for all turn parameters. We found that prospective fallers take longer to perform a turn, and their movements are less smooth when compared to non-fallers. A fall risk assessment model built only on turn parameters showed reasonable performance for both algorithms (AUC = 0.6). Our results show that differences between turn parameters in the algorithms, when averaged at the single-subject level, are less of a concern when looking for associations with prospective falls.

Paper Citation

in Harvard Style

Albites-Sanabria J., Palumbo P., Bandinelli S., Palmerini L. and Chiari L. (2023). Fall Risk Assessment Using Wearable-Based Turn Detection: Comparison of Different Algorithms During Real-World Monitoring. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS; ISBN 978-989-758-631-6, SciTePress, pages 294-300. DOI: 10.5220/0011727700003414

in Bibtex Style

@conference{biosignals23,
author={Jose Albites-Sanabria and Pierpaolo Palumbo and Stefania Bandinelli and Luca Palmerini and Lorenzo Chiari},
title={Fall Risk Assessment Using Wearable-Based Turn Detection: Comparison of Different Algorithms During Real-World Monitoring},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS},
year={2023},
pages={294-300},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011727700003414},
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 4: BIOSIGNALS
TI - Fall Risk Assessment Using Wearable-Based Turn Detection: Comparison of Different Algorithms During Real-World Monitoring
SN - 978-989-758-631-6
AU - Albites-Sanabria J.
AU - Palumbo P.
AU - Bandinelli S.
AU - Palmerini L.
AU - Chiari L.
PY - 2023
SP - 294
EP - 300
DO - 10.5220/0011727700003414
PB - SciTePress