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Machine-learning models for activity class prediction: A comparative study of feature selection and classification

Joana Chong1, Petra Tjurin2, Maisa Niemelä3

  • 1Faculty of Sciences, University of Lisbon, Lisbon, Portugal; Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.

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Summary
This summary is machine-generated.

Selecting specific time-domain features from raw accelerometry data improves machine-learning models for physical activity classification. Optimal feature subsets enhance prediction accuracy for artificial neural networks, support vector machines, and random forests.

Keywords:
Activity recognitionArtificial neural networkPhysical activityRandom ForestSupport vector machine

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Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Machine Learning

Background:

  • Machine learning (ML) models are increasingly used with accelerometry for physical activity classification.
  • However, the optimal features for maximizing predictive performance remain unclear.

Purpose of the Study:

  • To identify effective feature subsets and prediction algorithms for classifying physical activity using hip-worn raw acceleration data.
  • To determine the best combination of feature selection methods and ML classifiers.

Main Methods:

  • Raw acceleration data from 27 participants were used, split into training (70%) and validation (30%) sets.
  • 206 time-domain (TD) and frequency-domain (FD) features were extracted from 6-second windows.
  • Feature selection employed filter-based, wrapper-based, and embedded methods, followed by classification using artificial neural network (ANN), support vector machine (SVM), and random forest (RF).

Main Results:

  • Optimal feature subsets for ANN, SVM, and RF ranged from 20 to 45 features.
  • Classifiers trained with filter-based feature subsets achieved higher accuracy (78.1%-88%) compared to wrapper-based subsets (66%-83.5%).
  • Time-domain features reflecting signal variation, differences, and change frequency were most frequently selected.

Conclusions:

  • A curated subset of time-domain features from raw accelerometry is sufficient for accurate ML-based physical activity classification.
  • Proper selection of features across different axes is crucial for optimizing model performance.