Comparing Posture Classifications From Wearable Camera Still-images To Accelerometry

Abstract

PURPOSE: To assess the convergent validity of a novel image annotation schema for determining posture from wearable camera still-images (IMGs) and activPAL (AP) posture classifications. METHODS: Participants (n=5, mean age 45y, range 21-81y, 3F) wore an AP monitor at the right anterior-mid-thigh and an Autographer wearable camera (WC) above the xiphoid process during two or three, 2hr visits. WC was set to capture IMGs every 5-60 sec. IMGs were annotated with the Oxford Image Browser software for 3 postures that matched AP classifications: sitting, standing and movement. A sequence of 3 IMGs denoted a postural “event”. Sequences of fewer than 3 IMGs and when IMGs could not be accurately classified were annotated as “transitions”. For analyses, IMGs and AP output were converted to one-sec epochs and matched sec-by-sec. Total visit time and event time is reported in min. Overall and event percent agreement between AP and IMGs were calculated. Within events, statistical bias and CIs for posture times from IMGs to AP posture times were calculated to determine accuracy and precision with mean posture times from AP stated as reference. Confusion matrices were computed to determine misclassification. RESULTS: 13 visits were analyzed with a total visit time of 1546 min and total event time of 1375 min. Mean overall percent agreement including events and transitions was 72%, while mean event percent agreement was 80%. Fig.1 shows bias and mean event posture min from AP, where bias was low for sitting but IMGs tended to underestimate standing and overestimate movement. From confusion matrices, IMGs misclassify standing as movement 46% of the time. CONCLUSION: Within events, IMGs annotated with the novel schema can differentiate sitting and upright postures. Future work is warranted to both improve posture classification and examine IMG accuracy and precision in assessing activity behavior. Supported by NIH 1R01CA215318