A novel algorithm for determining the contextual characteristics of movement behaviors by combining accelerometer features and wireless beacons: development and implementation

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Magistro, D ORCID: https://orcid.org/0000-0002-2554-3701, Sessa, S, Kingsnorth, AP, Loveday, A, Simeone, A, Zecca, M and Esliger, DW, 2018. A novel algorithm for determining the contextual characteristics of movement behaviors by combining accelerometer features and wireless beacons: development and implementation. JMIR mHealth and uHealth, 6 (4): e100. ISSN 2291-5222

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Official URL: http://doi.org/10.2196/mhealth.8516

Abstract

Background: Unfortunately, global efforts to promote “how much” physical activity people should be undertaking have been largely unsuccessful. Given the difficulty of achieving a sustained lifestyle behavior change, many scientists are re-examining their approaches. One such approach is to focus on understanding the context of the lifestyle behavior (i.e., where, when, and with whom) with a view to identifying promising intervention targets.

Objective: The aim of this study was to develop and implement an innovative algorithm to determine “where” physical activity occurs using proximity sensors coupled with a widely used physical activity monitor.

Methods: A total of 19 Bluetooth beacons were placed in fixed locations within a multilevel, mixed-use building. In addition, 4 receiver-mode sensors were fitted to the wrists of a roving technician who moved throughout the building. The experiment was divided into 4 trials with different walking speeds and dwelling times. The data were analyzed using an original and innovative algorithm based on graph generation and Bayesian filters.

Results: Linear regression models revealed significant correlations between beacon-derived location and ground-truth tracking time, with intraclass correlations suggesting a high goodness of fit (R2=.9780). The algorithm reliably predicted indoor location, and the robustness of the algorithm improved with a longer dwelling time (>100 s; error <10%, R2=.9775). Increased error was observed for transitions between areas due to the device sampling rate, currently limited to 0.1 Hz by the manufacturer.

Conclusions: This study shows that our algorithm can accurately predict the location of an individual within an indoor environment. This novel implementation of “context sensing” will facilitate a wealth of new research questions on promoting healthy behavior change, the optimization of patient care, and efficient health care planning (e.g., patient-clinician flow, patient-clinician interaction).

Item Type:	Journal article
Publication Title:	JMIR mHealth and uHealth
Creators:	Magistro, D., Sessa, S., Kingsnorth, A.P., Loveday, A., Simeone, A., Zecca, M. and Esliger, D.W.
Publisher:	JMIR Publications
Date:	20 April 2018
Volume:	6
Number:	4
ISSN:	2291-5222
Identifiers:	Number Type 10.2196/mhealth.8516 DOI 29678806 PubMed ID
Rights:	© Daniele Magistro, Salvatore Sessa, Andrew P Kingsnorth, Adam Loveday, Alessandro Simeone, Massimiliano Zecca, Dale W Esliger. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 20.04.2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	09 May 2018 16:09
Last Modified:	10 May 2018 12:47
URI:	https://irep.ntu.ac.uk/id/eprint/33501