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Development of a multi-wear-site, deep learning-based physical activity intensity classification algorithm using raw

Johan Y Y Ng1, Joni H Zhang2, Stanley S Hui1

  • 1Department of Sports Science and Physical Education, The Chinese University of Hong Kong, Hong Kong, Hong Kong.

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New deep neural network models accurately classify accelerometer placement and activity intensity, outperforming existing methods. This advance allows for more participant autonomy and potentially more accurate physical activity data.

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Area of Science:

  • Biomedical engineering
  • Wearable technology
  • Machine learning for health

Background:

  • Accelerometers are common for measuring physical activity but rely on site-specific algorithms.
  • Non-compliance with wear instructions can lead to inaccurate activity intensity estimations.
  • Existing algorithms are limited by their dependence on precise device placement.

Purpose of the Study:

  • To develop deep neural network models for classifying accelerometer wear-site and activity intensity.
  • To evaluate the performance of these models against ground truth and existing count-based algorithms.

Main Methods:

  • Trained Long Short-Term Memory deep neural networks on raw acceleration data from 54 participants.
  • Collected activity data with accelerometers on hip, wrist, and chest.
  • Used portable COSMED K5 to measure metabolic equivalents for ground truth intensity.

Main Results:

  • Models achieved over 90% accuracy in classifying wear-sites and activity intensities.
  • Performance surpassed traditional count-based algorithms, which had lower accuracy.
  • Including participant age, height, and weight improved model accuracy to over 95%.

Conclusions:

  • Deep learning models offer accurate, non-wear-site-specific activity intensity classification.
  • These models outperform existing count-based algorithms.
  • Allowing participant autonomy in accelerometer placement can improve compliance and data accuracy.