Previously submitted to: JMIR Public Health and Surveillance (no longer under consideration since Oct 06, 2017)
Date Submitted: Oct 5, 2017
Open Peer Review Period: Oct 6, 2017 - Oct 6, 2017
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Automated Ecological Assessment of Physical Activity: Advancing Direct Observation
Background:
Technological advances provide opportunity for automating direct observation of physical activity, which would allow for continuous monitoring and feedback of physical activity in settings such as parks and schoolyards.
Objective:
This proof of concept study evaluated initial validity of computer vision algorithms for ecological assessment of physical activity.
Methods:
The sample comprised 6630 seconds per camera (3 cameras total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing an accelerometer to server as the ground truth for training and testing the computer vision algorithms. The algorithms were developed to assess, for every second of time, the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based METs. Mean differences (bias), mean absolute deviations (MAD), and intraclass correlation coefficients (ICC) assessed criterion validity compared to accelerometry separately for each camera and for 1-second and 1-minute intervals.
Results:
The number and proportion of participants sedentary, moderate, and in MVPA had small biases of ±0.20 people (±0.03 for proportion of people), and ICCs were very large (0.82-0.98). Light activity was slightly underestimated but had poor agreement (ICCs=0.14-0.36). Total METs were slightly underestimated by 9.3-17.1% and had good agreement (ICCs=0.68-0.79).
Conclusions:
Computer vision appears promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such systems can be used in application. Future research should capture a more diverse range of settings and activities to maximize generalizability of the algorithms. Use of such systems could support continuous monitoring and rapid feedback to prompt improvements in environments and programs.
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