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A novel ViT-BILSTM model for physical activity intensity classification in adults using gravity-based acceleration.

Lin Wang1, Zizhang Luo2, Tianle Zhang3

  • 1Faculty of Health and Life Sciences, University of Exeter, Heavitree Road, Exeter, EX1 2LU, UK. lw679@exeter.ac.uk.

BMC Biomedical Engineering
|January 31, 2025
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Summary

This study introduces a novel Vision Transformer (ViT) and bidirectional long short-term memory (Bi-LSTM) model for classifying physical activity intensity (PAI) using gravity-based acceleration, achieving high accuracy. The model demonstrates robust performance across various temporal windows and activity levels.

Keywords:
Deep learningGeneralisationPhysical activity patternsRaw accelerometer dataVariation

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Area of Science:

  • Wearable technology and human activity recognition
  • Machine learning for health and fitness monitoring
  • Biomedical signal processing and analysis

Background:

  • Accurate classification of physical activity intensity (PAI) is crucial for health monitoring and intervention.
  • Traditional methods often face challenges with complex movement patterns and varying intensities.
  • Gravity-based acceleration offers a rich data source for activity recognition.

Purpose of the Study:

  • To develop and validate a hybrid Vision Transformer (ViT) and bidirectional long short-term memory (Bi-LSTM) model for PAI classification.
  • To assess the model's performance using gravity-based acceleration data.
  • To investigate the impact of temporal window (TW) and PAI on classification accuracy.

Main Methods:

  • Utilized the Capture-24 dataset comprising raw accelerometer data from 151 adults.
  • Generated images from gravity-based acceleration to represent different PAIs.
  • Employed a ViT-BiLSTM model for image analysis and classification, comparing results with baseline models and evaluating robustness via temporal stability testing.

Main Results:

  • The ViT-BiLSTM model achieved a high overall accuracy of 98.5% ± 1.48% across five temporal windows (1s to 30s).
  • The model demonstrated superior accuracy in classifying sedentary activities (98.9% ± 1%) compared to light (98.2% ± 2%) and moderate-to-vigorous physical activity (98.2% ± 3%).
  • Analysis showed no significant variation in accuracy across different PAIs or TWs, with consistent performance improvement observed across epochs.

Conclusions:

  • The ViT-BiLSTM framework effectively classifies PAI using gravity-based acceleration, maintaining consistent performance across various TWs and intensities.
  • While performance is robust, slight variations may occur due to PAI and TW.
  • Future research should explore the influence of gravity-based acceleration on PAI thresholds to further enhance model robustness and reliability.