Tools
Tools
Magistro, D. 
ORCID: 0000-0002-2554-3701, Sessa, S., Kingsnorth, A.P., Loveday, A., Simeone, A., Zecca, M. and Esliger, D.W.,
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
|
Text
10998_Magistro.pdf - Published version Download (2MB) | Preview |
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: |
|
||||||
| 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 |
Actions (login required)
 |
Edit View |
Views
Views per month over past year
Downloads
Downloads per month over past year
