Understanding of human behaviors from videos in nursing care monitoring systems

  • Authors:

  • Chin-De Liu;Pau-Choo Chung;Yi-Nung Chung;Monique Thonnat

  • Affiliations:

  • Department of Electrical Engineering, Institute of Computer and Communication Engineering, National Cheng Kung University, Tainan 70101, Taiwan ROC;(Correspd. pcchung@ee.ncku.edu.tw) Department of Electrical Engineering, Institute of Computer and Communication Engineering, National Cheng Kung University, Tainan 70101, Taiwan ROC;Department of Electrical Engineering, Dayeh University, Changhua 515, Taiwan ROC;Orion research team, INRIA BP93, FR 06902 Sophia Antipolis, France

  • Venue:
  • Journal of High Speed Networks - Broadband Multimedia Sensor Networks in Healthcare Applications
  • Year:
  • 2007

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Abstract

This paper addresses the issue in scenario-based understanding of human behavior from videos in a nursing care monitoring system. The analysis is carried out based on experiments consisting of single-state scenarios and multi-state scenarios where the former monitors activities under contextual contexts for elementary behavior reasoning, while the latter dictating the elementary behavior order for behavior reasoning, with a priori knowledge in system profile for normality detection. By integrating the activities, situation context, and profile knowledge we can have a better understanding of patients in a monitoring system. In activity recognition, a Negation-Selection mechanism is developed. which employs a divide-and-conquer concept with the Negation using posture transition to preclude the negative set from the activities. The Selection that follows the Negation uses a moving history trace for activity recognition. Such a history trace composes not only the pose from single frame, but also history trajectory information. As a result, the activity can be more accurately identified. The developed approach has been established into a nursing care monitoring system for elder's daily life behaviors. Results have shown the promise of the approach which can accurately interpret 85% of the regular daily behavior. In addition, the approach is also applied to accident detection which was found to have 90% accuracy with 0% false alarm.