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

Toby G Pavey1, Nicholas D Gilson2, Sjaan R Gomersall2

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Wrist accelerometers accurately classify physical activity in lab settings. However, free-living validation showed modest accuracy for step-based activity detection, suggesting further refinement is needed for real-world use.

Keywords:
AccelerometerPhysical activityRandom forest classifierWrist

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Area of Science:

  • Biomedical Engineering
  • Physical Activity Measurement
  • Wearable Technology

Background:

  • Wrist-worn accelerometers offer convenience and high wear-time compliance for activity monitoring.
  • Previous research predominantly used laboratory-based, choreographed activities for model training and testing.
  • Emerging evidence suggests the need to evaluate accelerometer validity in free-living conditions.

Purpose of the Study:

  • To develop and validate a random forest activity classifier using wrist accelerometer data.
  • To assess the performance of laboratory-trained models in real-world, free-living environments.

Main Methods:

  • Twenty-one participants underwent laboratory activity trials and a 24-hour free-living monitoring period.
  • A GENEActiv monitor on the non-dominant wrist collected data.
  • Random forest models were trained on time and frequency domain features from 10-second windows to classify sedentary, stationary+, walking, and running activities.
  • Model performance was cross-validated and compared against activPAL monitors during the free-living trial.

Main Results:

  • The random forest classifier achieved 92.7% overall accuracy in laboratory settings.
  • Recognition accuracies for specific activities in the lab were: sedentary (80.1%), stationary+ (95.7%), walking (91.7%), and running (93.7%).
  • Excellent agreement (>90%) with activPAL monitors was observed for stepping vs. non-stepping during free-living, with an Intraclass Correlation Coefficient (ICC) of 0.92 for stepping time. However, sensitivity and positive predictive values were modest, with a mean bias of 10.3 min/day.

Conclusions:

  • The random forest classifier demonstrates high accuracy for group-level activity predictions in controlled laboratory settings.
  • Performance in free-living conditions was less accurate for distinguishing stepping from non-stepping behaviors.
  • Future research should prioritize rigorous field-based evaluations using observational data as a criterion measure.