Predicting lying, sitting, walking and running using Apple Watch and Fitbit data.

Autor:	Fuller D; School of Human Kinetics and Recreation, Memorial University of Newfoundland, St. John's, Newfoundland, Canada.; Department of Computer Science, Faculty of Science, Memorial University of Newfoundland, St. John's, Newfoundland, Canada., Anaraki JR; Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada., Simango B; School of Human Kinetics and Recreation, Memorial University of Newfoundland, St. John's, Newfoundland, Canada., Rayner M; School of Human Kinetics and Recreation, Memorial University of Newfoundland, St. John's, Newfoundland, Canada., Dorani F; Department of Computer Science, Faculty of Science, Memorial University of Newfoundland, St. John's, Newfoundland, Canada., Bozorgi A; Department of Computer Science, Faculty of Science, Memorial University of Newfoundland, St. John's, Newfoundland, Canada., Luan H; Department of Geography, University of Oregon, Eugene, Oregon, USA., A Basset F; School of Human Kinetics and Recreation, Memorial University of Newfoundland, St. John's, Newfoundland, Canada.
Jazyk:	angličtina
Zdroj:	BMJ open sport & exercise medicine [BMJ Open Sport Exerc Med] 2021 Apr 08; Vol. 7 (1), pp. e001004. Date of Electronic Publication: 2021 Apr 08 (Print Publication: 2021).
DOI:	10.1136/bmjsem-2020-001004
Abstrakt:	Objectives: This study's objective was to examine whether commercial wearable devices could accurately predict lying, sitting and varying intensities of walking and running. Methods: We recruited a convenience sample of 49 participants (23 men and 26 women) to wear three devices, an Apple Watch Series 2, a Fitbit Charge HR2 and iPhone 6S. Participants completed a 65 min protocol consisting of 40 min of total treadmill time and 25 min of sitting or lying time. The study's outcome variables were six movement types: lying, sitting, walking self-paced and walking/running at 3 metabolic equivalents of task (METs), 5 METs and 7 METs. All analyses were conducted at the minute level with heart rate, steps, distance and calories from Apple Watch and Fitbit. These included three different machine learning models: support vector machines, Random Forest and Rotation forest. Results: Our dataset included 3656 and 2608 min of Apple Watch and Fitbit data, respectively. Rotation Forest models had the highest classification accuracies for Apple Watch at 82.6%, and Random Forest models had the highest accuracy for Fitbit at 90.8%. Classification accuracies for Apple Watch data ranged from 72.6% for sitting to 89.0% for 7 METs. For Fitbit, accuracies varied between 86.2% for sitting to 92.6% for 7 METs. Conclusion: This preliminary study demonstrated that data from commercial wearable devices could predict movement types with reasonable accuracy. More research is needed, but these methods are a proof of concept for movement type classification at the population level using commercial wearable device data. Competing Interests: Competing interests: None declared. (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu