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Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data.

Yordanka Karayaneva1, Sara Sharifzadeh2, Yanguo Jing3

  • 1School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK.

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Summary
This summary is machine-generated.

This study demonstrates low-resolution infrared (LRIR) imaging for human activity recognition (HAR), achieving improved accuracy with a novel noise reduction technique. Optimal sensor placement was identified for e-healthcare applications.

Keywords:
AI-enabled healthcareclassificationfeature extractionhuman activity recognition (HAR)infrared sensorsnoise reduction

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Biomedical Engineering

Background:

  • Human Activity Recognition (HAR) is crucial for e-healthcare.
  • Low-resolution infrared (LRIR) imaging offers potential for non-intrusive monitoring.
  • Challenges include noise and optimal data acquisition for LRIR-based HAR.

Purpose of the Study:

  • To evaluate the feasibility of LRIR image streams for HAR.
  • To develop and validate a novel noise reduction technique for LRIR data.
  • To determine optimal sensor configurations for effective HAR.

Main Methods:

  • Utilized synchronized multichannel LRIR sensor systems.
  • Developed a periodic noise reduction technique for spatiotemporal activity profiles.
  • Compared manual feature extraction (SVM, RF, k-NN, LR) with deep learning (CNN-LSTM).

Main Results:

  • The proposed noise reduction technique improved performance by up to 14.15%.
  • Optimal results were achieved with a single sensor at close proximity.
  • Models demonstrated robustness to sensor displacement and multi-subject detection.

Conclusions:

  • LRIR imaging is a feasible approach for HAR in e-healthcare.
  • The novel noise reduction method enhances LRIR-based HAR accuracy.
  • Optimized sensor configurations and robust models are key for practical deployment.