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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.

Sensors (Basel, Switzerland)

|April 17, 2019

Summary

This summary is machine-generated.

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.

Keywords:

LAC associative classifiers context awareness elderly people healthcare smart homes wellness determination

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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.