A Novel Algorithm for Determining the Contextual Characteristics of Movement Behaviors by Combining Accelerometer Features and Wireless Beacons: Development and Implementation

Abstract

Background: Unfortunately, global efforts to promote “how much” physical activity people should be undertaking have beenlargely unsuccessful. Given the difficulty of achieving a sustained lifestyle behavior change, many scientists are reexaminingtheir approaches. One such approach is to focus on understanding the context of the lifestyle behavior (ie, where, when, and withwhom) 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 activityoccurs 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 wasdivided into 4 trials with different walking speeds and dwelling times. The data were analyzed using an original and innovativealgorithm based on graph generation and Bayesian filters.Results: Linear regression models revealed significant correlations between beacon-derived location and ground-truth trackingtime, 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 wasobserved 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 indoorenvironment. This novel implementation of “context sensing” will facilitate a wealth of new research questions on promotinghealthy behavior change, the optimization of patient care, and efficient health care planning (eg, patient-clinician flow,patient-clinician interaction).

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ORCID for Daniele Magistro: orcid.org/0000-0002-2554-3701

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