Validation of Wearable Camera Still Images to Assess Posture in Free-Living Conditions

in Journal for the Measurement of Physical Behaviour
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  • 1 University of Wisconsin-Milwaukee
  • 2 University of Oxford
  • 3 National Institute of Health Research Oxford Biomedical Research Centre
  • 4 University of Massachusetts-Amherst
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Purpose: To assess the convergent validity of body-worn wearable camera still images (IMGs) for determining posture compared with activPAL (AP) classifications. Methods: The participants (n = 16, mean age 46.7 ± 23.8 years, 9 F) wore an Autographer wearable camera and an AP during three 2-hr free-living visits. IMGs were on average 8.47 s apart and were annotated with output consisting of events, transitory states, unknown, and gaps. The events were annotations that matched AP classifications (sit, stand, and move), consisting of at least three IMGs; the transitory states were posture annotations fewer than three IMGs; the unknowns were IMGs that could not be accurately classified; and the gaps were the time between annotations. For the analyses, the annotation and AP output were converted to 1-s epochs. The total and average length of visits and events were reported in minutes. Bias and 95% confidence intervals for event posture times from IMGs to AP were calculated to determine accuracy and precision. Confusion matrices using total AP posture times were computed to determine misclassification. Results: Forty-three visits were analyzed, with a total visit and event time of 5,027.73 and 4,237.23 min, respectively, and the average visit and event lengths being 116.92 and 98.54 min, respectively. Bias was not statistically significant for sitting, but was significant for standing and movement (0.84, −6.87, and 6.04 min, respectively). From confusion matrices, IMGs correctly classified sitting, standing, and movement (85.69%, 54.87%, and 69.41%, respectively) of total AP time. Conclusion: Wearable camera IMGs provide a good estimation of overall sitting time. Future work is warranted to improve posture classifications and examine the validity of IMGs in assessing activity-type behaviors.

Martinez, Decker, Cho, Swartz, and Strath are with the Department of Kinesiology, University of Wisconsin-Milwaukee, WI, USA. Doherty is with the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, England, United Kingdom; and the National Institute of Health Research Oxford Biomedical Research Centre, Oxford, United Kingdom. Staudenmayer is with the Department of Statistics and Mathematics, University of Massachusetts-Amherst, MA, USA.

Martinez (marti994@uwm.edu) is corresponding author.
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