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A model for abnormal activity recognition and alert generation system for elderly care by hidden conditional random

Zafar Ali Khan1, Won Sohn

  • 1Department of Electronics and Radio Engineering, Kyung Hee University, Yongin, South Korea.

Telemedicine Journal and E-Health : the Official Journal of the American Telemedicine Association
|September 8, 2012
PubMed
Summary

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This study introduces an improved human activity recognition (HAR) system using vision sensors for elderly care. The system effectively detects abnormal activities, enhancing safety for seniors living alone.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Increasing elderly population living alone necessitates advanced healthcare monitoring.
  • Vision sensor-based human activity recognition (HAR) is crucial for remote elderly care.
  • This study addresses the need for reliable automatic detection of abnormal activities in home environments.

Purpose of the Study:

  • To develop and validate an improved HAR system for recognizing daily activities of elderly individuals.
  • To generate timely alerts for abnormal activities, enhancing elderly safety and care.
  • To assess the system's performance across various activities and view angles.

Main Methods:

  • Utilized R-transform for feature extraction and Generalized Discriminant Analysis (GDA) for dimension reduction.

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  • Employed Linde-Buzo-Gray algorithm for quantifying silhouette sequences.
  • Applied hidden conditional random fields for activity recognition from multiple view angles.

    • Main Results:

    • Achieved an average recognition rate of 94.2% for abnormal activities.
    • Attained an average recognition rate of 92.7% for normal activities.
    • Demonstrated high accuracy in distinguishing between highly similar activities from diverse angles.

    Conclusions:

    • The proposed HAR system demonstrates flexibility and efficacy in recognizing abnormal activities for elderly care.
    • The system's ability to generate alerts for critical events enhances patient safety.
    • The findings support the use of advanced vision-based HAR for independent elderly living.