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

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• 作者：Toby G. Pavey^a ; ^b ; ^{toby.pavey@qut.edu.au} ; Nicholas D. Gilson^b ; Sjaan R. Gomersall^b ; Bronwyn Clark^c ; Stewart G. Trost^a
• 关键词：Accelerometer ; Random forest classifier ; Physical activity ; Wrist
• 刊名：Journal of Science and Medicine in Sport
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：20
• 期：1
• 页码：75-80
• 全文大小：514 K
• 卷排序：20

文摘

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.DesignTwenty-one participants (mean age = 27.6 ± 6.2) completed seven lab-based activity trials and a 24 h free-living trial (N = 16).MethodsParticipants 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.ResultsOverall 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).ConclusionsThe 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.