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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer Data.

Eduardo Gomes1, Luciano Bertini1, Wagner Rangel Campos1

  • 1Computer Science Departament, Fluminense Federal University, Rio das Ostras 28895-532, Brazil.

Sensors (Basel, Switzerland)
|February 12, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces Activity-Intensity recognition using accelerometer data for better patient monitoring. Combining activity and intensity recognition improves daily activity descriptions, aiding healthcare professionals.

Keywords:
accelerometersactivity and intensity recognitionmachine learningmobile computingpervasive healthcare monitoring

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Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Health Informatics

Background:

  • Activity recognition is crucial for pervasive healthcare monitoring.
  • Intensity recognition, a key contextual parameter, is less explored.
  • Accelerometer data can provide insights into daily activities.

Purpose of the Study:

  • To investigate the advantage of coupling activity and intensity recognition (Activity-Intensity) using accelerometer data.
  • To compare two supervised classification approaches: single classifier vs. separate classifiers.
  • To evaluate the performance of k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF) algorithms.

Main Methods:

  • Collected accelerometer data to capture daily activities.
  • Implemented a single classifier approach for joint Activity-Intensity recognition.
  • Implemented a two-classifier approach for separate activity and intensity recognition.
  • Utilized KNN, SVM, and RF algorithms for classification tasks.

Main Results:

  • Both single and separate classifier approaches demonstrated viability for Activity-Intensity recognition.
  • The single KNN classifier achieved 79% accuracy for coupled Activity-Intensity recognition.
  • The separate classifier approach yielded 97% accuracy for activity (RF) and 80% for intensity (KNN), resulting in 78% for coupled recognition.

Conclusions:

  • Coupling activity and intensity recognition enhances the description of daily activities.
  • The single KNN classifier approach is effective for Activity-Intensity recognition.
  • Findings support the development of decision systems for health professionals to improve movement evaluation.