Field evaluation of a random forest activity classifier for wrist-worn accelerometer data

Objectives Wrist-worn accelerometers are convenient to wear and associated with greater wear-time compliance. Previous work has generally relied on choreographed activity trials to train and test classification models. However, validity in free-living contexts is starting to emerge. Study aims were: (1) train and test a random forest activity classifier for wrist accelerometer data; and (2) determine if models trained on laboratory data perform well under free-living conditions. Design Twenty-one participants (mean age = 27.6 ± 6.2) completed seven lab-based activity trials and a 24 h free-living trial (N = 16). Methods Participants wore a GENEActiv monitor on the non-dominant wrist. Classification models recognising four activity classes (sedentary, stationary+, walking, and running) were trained using time and frequency domain features extracted from 10-s non-overlapping windows. Model performance was evaluated using leave-one-out-cross-validation. Models were implemented using the randomForest package within R. Classifier accuracy during the 24 h free living trial was evaluated by calculating agreement with concurrently worn activPAL monitors. Results Overall classification accuracy for the random forest algorithm was 92.7%. Recognition accuracy for sedentary, stationary+, walking, and running was 80.1%, 95.7%, 91.7%, and 93.7%, respectively for the laboratory protocol. Agreement with the activPAL data (stepping vs. non-stepping) during the 24 h free-living trial was excellent and, on average, exceeded 90%. The ICC for stepping time was 0.92 (95% CI = 0.75–0.97). However, sensitivity and positive predictive values were modest. Mean bias was 10.3 min/d (95% LOA = −46.0 to 25.4 min/d). Conclusions The random forest classifier for wrist accelerometer data yielded accurate group-level predictions under controlled conditions, but was less accurate at identifying stepping verse non-stepping behaviour in free living conditions Future studies should conduct more rigorous field-based evaluations using observation as a criterion measure.