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Activity Detection in Indoor Environments Using Multiple 2D Lidars.

Mondher Bouazizi1, Alejandro Lorite Mora2, Kevin Feghoul3

  • 1Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
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This study introduces a novel health monitoring system using multiple 2D Light Detection and Ranging (Lidar) sensors to detect falls and monitor elderly activity. The system achieves high accuracy, offering unobtrusive and reliable elderly care solutions.

Area of Science:

  • Engineering
  • Computer Science
  • Gerontology

Background:

  • Effective elderly health monitoring requires unobtrusive, continuous activity tracking to detect hazardous events like falls.
  • Current non-contact sensor systems face limitations due to environmental obstacles.

Purpose of the Study:

  • To propose and evaluate a novel activity detection method for elderly health monitoring using multiple 2D Lidar sensors.
  • To overcome environmental limitations in sensor-based activity detection.

Main Methods:

  • Utilizing multiple 2D Lidar sensors in indoor environments with obstacles.
  • Concatenating Lidar data and transforming it into image-like representations.
  • Employing a convolutional Long Short-Term Memory (LSTM) Neural Network for activity classification.
Keywords:
2D Lidaractivity detectiondeep learningfall detectionhealthcarehuman activity recognitionmachine learning

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Main Results:

  • Achieved high accuracy in activity detection (96.10%), fall detection (99.13%), and unsteady gait detection (93.13%).
  • Demonstrated the system's effectiveness in environments with varying obstacles.
  • Validated the efficacy of the proposed Lidar-based approach.

Conclusions:

  • The proposed 2D Lidar-based system offers a promising, non-intrusive solution for elderly health monitoring.
  • The collaborative multi-sensor approach effectively addresses environmental limitations.
  • This technology can significantly enhance the safety and well-being of the elderly.