research-article

Unsupervised detection and analysis of changes in everyday physical activity data

Authors:

Maureen Schmitter-EdgecombeAuthors Info & Claims

Journal of Biomedical Informatics, Volume 63, Issue C

Pages 54 - 65

https://doi.org/10.1016/j.jbi.2016.07.020

Published: 01 October 2016 Publication History

Abstract

Display Omitted A framework for detecting and analyzing changes in physical activity time series data.A method to evaluate if the detected changes are representative of a significant lifestyle modification.Evaluation based on synthetic data and real-world Fitbit data collected from older adults. Sensor-based time series data can be utilized to monitor changes in human behavior as a person makes a significant lifestyle change, such as progress toward a fitness goal. Recently, wearable sensors have increased in popularity as people aspire to be more conscientious of their physical health. Automatically detecting and tracking behavior changes from wearable sensor-collected physical activity data can provide a valuable monitoring and motivating tool. In this paper, we formalize the problem of unsupervised physical activity change detection and address the problem with our Physical Activity Change Detection (PACD) approach. PACD is a framework that detects changes between time periods, determines significance of the detected changes, and analyzes the nature of the changes. We compare the abilities of three change detection algorithms from the literature and one proposed algorithm to capture different types of changes as part of PACD. We illustrate and evaluate PACD on synthetic data and using Fitbit data collected from older adults who participated in a health intervention study. Results indicate PACD detects several changes in both datasets. The proposed change algorithms and analysis methods are useful data mining techniques for unsupervised, window-based change detection with potential to track users physical activity and motivate progress toward their health goals.

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Cited By

Cook DStrickland MSchmitter-Edgecombe M(2022)Detecting Smartwatch-Based Behavior Change in Response to a Multi-Domain Brain Health InterventionACM Transactions on Computing for Healthcare10.1145/35080203:3(1-18)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3508020
Aloulou HMokhtari MAbdulrazak B(2019)Deployment of an IoT Solution for Early Behavior Change DetectionHow AI Impacts Urban Living and Public Health10.1007/978-3-030-32785-9_3(27-35)Online publication date: 14-Oct-2019
https://dl.acm.org/doi/10.1007/978-3-030-32785-9_3
Hossain HAl Haiz Khan MRoy N(2018)DeActiveProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32142692:2(1-23)Online publication date: 5-Jul-2018
https://dl.acm.org/doi/10.1145/3214269

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cover image Journal of Biomedical Informatics

Journal of Biomedical Informatics Volume 63, Issue C

October 2016

373 pages

ISSN:1532-0464

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Copyright © Elsevier Inc.

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Elsevier Science

San Diego, CA, United States

Publication History

Published: 01 October 2016

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Cited By

Cook DStrickland MSchmitter-Edgecombe M(2022)Detecting Smartwatch-Based Behavior Change in Response to a Multi-Domain Brain Health InterventionACM Transactions on Computing for Healthcare10.1145/35080203:3(1-18)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3508020
Aloulou HMokhtari MAbdulrazak B(2019)Deployment of an IoT Solution for Early Behavior Change DetectionHow AI Impacts Urban Living and Public Health10.1007/978-3-030-32785-9_3(27-35)Online publication date: 14-Oct-2019
https://dl.acm.org/doi/10.1007/978-3-030-32785-9_3
Hossain HAl Haiz Khan MRoy N(2018)DeActiveProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32142692:2(1-23)Online publication date: 5-Jul-2018
https://dl.acm.org/doi/10.1145/3214269

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