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Design and Analysis for Fall Detection System Simplification

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HMM-Based Action Recognition System for Elderly Healthcare by Colorizing Depth Map.

Ye Htet¹, Thi Thi Zin², Pyke Tin³

¹Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Miyazaki 889-2192, Japan.

International Journal of Environmental Research and Public Health

|October 14, 2022

Summary

This summary is machine-generated.

This study introduces a novel real-time action recognition system for elderly care, utilizing Hidden Markov Models (HMM) and depth maps for privacy-preserving monitoring. The system achieved 84.04% accuracy when fused with Support Vector Machines (SVM).

Keywords:

Hidden Markov Model Histogram of Oriented Gradients Support Vector Machine Viterbi Algorithm YOLOv5 action recognition depth colorization e-Healthcare older persons person detection

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Area of Science:

Computer Vision
Artificial Intelligence
Gerontology

Background:

Elderly care requires reliable monitoring systems, but current action recognition technology is not suitable for continuous, automated use.
Existing methods often compromise privacy or lack efficiency in real-world applications.

Purpose of the Study:

To develop and validate a novel, real-time action recognition system for elderly care applications.
To enhance privacy protection and operational efficiency in continuous monitoring.

Main Methods:

A real-time action recognition system integrating Hidden Markov Models (HMM) with colorized depth maps.
Privacy-preserving person detection using You Only Look Once (YOLOv5) on depth data.
Feature extraction via Histogram of Oriented Gradients (HOG) from depth map sequences, processed by HMM and Viterbi Algorithm for action recognition.

Main Results:

The system demonstrated effective action recognition on real-world data from three participants in a care center.
Fusion of HMM with Support Vector Machine (SVM) yielded the highest average accuracy of 84.04% among tested classification algorithms.

Conclusions:

The developed system provides a robust and privacy-conscious solution for real-time action recognition in elderly care settings.
The HMM-SVM fusion approach shows significant promise for improving automated monitoring and support for the elderly.