RT Journal Article
SR Electronic
T1 Predicting lying, sitting, walking and running using Apple Watch and Fitbit data
JF BMJ Open Sport & Exercise Medicine
JO BMJ OPEN SP EX MED
FD BMJ Publishing Group Ltd
SP e001004
DO 10.1136/bmjsem-2020-001004
VO 7
IS 1
A1 Fuller, Daniel
A1 Anaraki, Javad Rahimipour
A1 Simango, Bongai
A1 Rayner, Machel
A1 Dorani, Faramarz
A1 Bozorgi, Arastoo
A1 Luan, Hui
A1 A Basset, Fabien
YR 2021
UL http://bmjopensem.bmj.com/content/7/1/e001004.abstract
AB 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.Data are available in a public, open access repository. Data are available at this link: https://doi.org/10.7910/DVN/ZS2Z2J.