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A Context-Aware Accurate Wellness Determination (CAAWD) Model for Elderly People Using Lazy Associative
Farhan Sabir Ujager1, Azhar Mahmood2
1Faculty of Computing and Engineering Sciences, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad 44000, Pakistan. farhan@biit.edu.pk.
This study introduces a context-aware accurate wellness determination (CAAWD) model using Wireless Sensor Network (WSN) technology for elderly healthcare. The CAAWD model non-intrusively monitors daily behavior patterns for improved wellness assessment.
Area of Science:
- Gerontology
- Computer Science
- Healthcare Technology
Background:
- Wireless Sensor Network (WSN) smart homes offer potential for elderly healthcare.
- Existing methods like wearables have usability issues, and visual monitoring raises privacy concerns.
Purpose of the Study:
- To present a context-aware accurate wellness determination (CAAWD) model for elderly individuals.
- To enable non-obtrusive monitoring of daily behavior patterns using simple sensor nodes.
Main Methods:
- Developed a context-aware accurate wellness determination (CAAWD) model.
- Proposed a contextual data extraction algorithm (CDEA) for generating behavior-training instances.
- Utilized a lazy associative classifier (LAC) for classifying frequent behavioral patterns.
Main Results:
- The CDEA extracts spatial-temporal and contextual information for accurate wellness classification.
- LAC integrates contextual attributes for behavior-focused classification rules, offering high accuracy and efficiency.
- The CAAWD model demonstrated superior performance over existing techniques in accuracy, precision, and f-measure.
Conclusions:
- The CAAWD model provides an accurate, non-obtrusive, and privacy-preserving method for elderly wellness determination.
- Integration of spatial-temporal data, efficient classification, and contextual validation enhances model reliability.
- This approach offers a significant advancement in smart home healthcare for the elderly.
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