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Detection abnormal pattern in activities of daily living using sequence alignment method.

Ho-Youl Jung¹, Seon-Hee Park, Soo Joon Park

¹BT Convergence Technology Research Department of Electronics and Telecommunications Research Institute, Gajeong-dong, Yuseong-gu, Daejeon, Republic of Korea. hoyoul.jung@etri.re.kr

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 24, 2009

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As the global population ages, monitoring daily activities is crucial for elder care. New computational methods are needed to detect abnormal health signs by analyzing changes in daily living patterns.

Area of Science:

Gerontology and Health Informatics
Computational Health Monitoring

Background:

Increasing global aging population necessitates advanced elder care solutions.
Current care systems track daily activities but struggle to identify abnormal health states.
Existing technologies lack the ability to interpret the significance of activity sequences for health status.

Purpose of the Study:

To develop a novel computational method for detecting abnormal signs in elderly individuals.
To analyze changes in sequences of activities of daily living (ADLs) for health monitoring.
To enhance the capabilities of current care services beyond simple activity tracking.

Main Methods:

Utilizing data tracking and monitoring of daily activities.
Applying computational analysis to recognize patterns in ADLs.

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Developing algorithms to detect deviations from normal activity sequences.

Main Results:

The study focuses on the necessity of developing new methods.
The proposed approach aims to identify abnormal signs by analyzing ADL sequences.
This research addresses the limitations of current systems in health state detection.

Conclusions:

A new computational method is required to effectively monitor the health of the elderly.
Analyzing sequences of daily living activities can reveal critical health changes.
This approach promises to improve the detection of abnormal states in care services.