期刊
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
卷 15, 期 3, 页码 438-448出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITB.2011.2113352
关键词
Abnormal behavior pattern; daily activity monitoring; elderly healthcare; support vector data description (SVDD)
类别
资金
- Advanced Biometric Research Center (ABRC)
- Korea Science and Engineering Foundation (KOSEF)
- National Research Foundation of Korea [2001-0036767] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
In this study, we developed an automated behavior analysis system using infrared (IR) motion sensors to assist the independent living of the elderly who live alone and to improve the efficiency of their healthcare. An IR motion-sensor-based activity-monitoring system was installed in the houses of the elderly subjects to collect motion signals and three different feature values, activity level, mobility level, and nonresponse interval (NRI). These factors were calculated from the measured motion signals. The support vector data description (SVDD) method was used to classify normal behavior patterns and to detect abnormal behavioral patterns based on the aforementioned three feature values. The simulation data and real data were used to verify the proposed method in the individual analysis. A robust scheme is presented in this paper for optimally selecting the values of different parameters especially that of the scale parameter of the Gaussian kernel function involving in the training of the SVDD window length, T of the circadian rhythmic approach with the aim of applying the SVDD to the daily behavior patterns calculated over 24 h. Accuracies by positive predictive value (PPV) were 95.8% and 90.5% for the simulation and real data, respectively. The results suggest that the monitoring system utilizing the IR motion sensors and abnormal-behavior-pattern detection with SVDD are effective methods for home healthcare of elderly people living alone.
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