DOI: 10.3390/ijerph14121487; PMCID: PMC5750905
Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised 6630 seconds per camera (three cameras in total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing accelerometers. Computer vision algorithms were developed to assess the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based metabolic equivalents of tasks (MET)-minutes. Means and standard deviations (SD) of bias/difference values, and intraclass correlation coefficients (ICC) assessed the criterion validity compared to accelerometry separately for each camera. The number and proportion of participants sedentary and in moderate-to-vigorous physical activity (MVPA) had small biases (within 20% of the criterion mean) and the ICCs were excellent (0.82-0.98). Total MET-minutes were slightly underestimated by 9.3-17.1% and the ICCs were good (0.68-0.79). The standard deviations of the bias estimates were moderate-to-large relative to the means. The computer vision algorithms appeared to have acceptable sample-level validity (i.e., across a sample of time intervals) and are promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such tools can be used in a diverse range of settings.
Int J Environ Res Public Health
Accelerometry; Adult; Algorithms; Exercise; Female; Humans; Male; Middle Aged; Pilot Projects; Posture; Sedentary Behavior; Young Adult
accelerometry; exercise; measurement; parks; public health
Carlson, J. A., Liu, B., Sallis, J. F., Kerr, J., Hipp, J. A., Staggs, V. S., Papa, A., Dean, K., Vasconcelos, N. M. Automated Ecological Assessment of Physical Activity: Advancing Direct Observation. Int J Environ Res Public Health 14, (2017).