Research
Using wearable cameras to categorise type and context of accelerometer-identified episodes of physical activity
1 British Heart Foundation Health Promotion Research Group, Department of Public Health, University of Oxford, Oxford, UK
2 SDU, University of California San Diego, La Jolla, USA
3 Centre for Physical Activity and Nutrition, Auckland University of Technology, Auckland, New Zealand
4 McCaughey VicHealth Centre for the Promotion of Mental Health and Community Wellbeing, the University of Melbourne, Parkville, Australia
5 CLARITY: Centre for Sensor Web Technologies, Dublin City University, Dublin, Ireland
International Journal of Behavioral Nutrition and Physical Activity 2013, 10:22 doi:10.1186/1479-5868-10-22
Published: 13 February 2013Abstract
Background
Accelerometers can identify certain physical activity behaviours, but not the context in which they take place. This study investigates the feasibility of wearable cameras to objectively categorise the behaviour type and context of participants’ accelerometer-identified episodes of activity.
Methods
Adults were given an Actical hip-mounted accelerometer and a SenseCam wearable camera (worn via lanyard). The onboard clocks on both devices were time-synchronised. Participants engaged in free-living activities for 3 days. Actical data were cleaned and episodes of sedentary, lifestyle-light, lifestyle-moderate, and moderate-to-vigorous physical activity (MVPA) were identified. Actical episodes were categorised according to their social and environmental context and Physical Activity (PA) compendium category as identified from time-matched SenseCam images.
Results
There were 212 days considered from 49 participants from whom SenseCam images and associated Actical data were captured. Using SenseCam images, behaviour type and context attributes were annotated for 386 (out of 3017) randomly selected episodes (such as walking/transportation, social/not-social, domestic/leisure). Across the episodes, 12 categories that aligned with the PA Compendium were identified, and 114 subcategory types were identified. Nineteen percent of episodes could not have their behaviour type and context categorized; 59% were outdoors versus 39% indoors; 33% of episodes were recorded as leisure time activities, with 33% transport, 18% domestic, and 15% occupational. 33% of the randomly selected episodes contained direct social interaction and 22% were in social situations where the participant wasn’t involved in direct engagement.
Conclusion
Wearable camera images offer an objective method to capture a spectrum of activity behaviour types and context across 81% of accelerometer-identified episodes of activity. Wearable cameras represent the best objective method currently available to categorise the social and environmental context of accelerometer-defined episodes of activity in free-living conditions.
