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J Sci Med Sport. 2017 Jan;20(1):75-80. doi: 10.1016/j.jsams.2016.06.003. Epub 2016 Jun 23.

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

Author information

1
School of Exercise and Nutrition Sciences, Queensland University of Technology, Australia; School of Human Movement and Nutrition Sciences, The University of Queensland, Australia. Electronic address: toby.pavey@qut.edu.au.
2
School of Human Movement and Nutrition Sciences, The University of Queensland, Australia.
3
School of Public Health, The University of Queensland, Australia.
4
School of Exercise and Nutrition Sciences, Queensland University of Technology, Australia.

Abstract

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 24h 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 24h 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 24h 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 10.3min/d (95% LOA=-46.0 to 25.4min/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.

KEYWORDS:

Accelerometer; Physical activity; Random forest classifier; Wrist

PMID:
27372275
DOI:
10.1016/j.jsams.2016.06.003
[Indexed for MEDLINE]

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