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Mining Productive-Associated Periodic-Frequent Patterns in Body Sensor Data for Smart Home Care
Walaa N Ismail1, Mohammad Mehedi Hassan2
1Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia. 435204202@student.ksu.edu.sa.
Sensors (Basel, Switzerland)
|April 27, 2017
Summary
This study introduces an efficient method for analyzing vital sign data from body sensor networks (BSNs). The PPFP-growth algorithm identifies crucial health patterns, enhancing remote patient monitoring and smart healthcare decisions.
Area of Science:
- Biomedical Engineering
- Health Informatics
- Data Mining
Background:
- Continuous health monitoring using body sensor networks (BSNs) generates vital sign data crucial for healthcare decision-making.
- Detecting unusual or critical situations in real-time is essential for providing timely assistance, particularly in smart home environments for elderly care.
- Understanding associations within vital sign data can significantly improve diagnosis, treatment, and smart care quality.
Purpose of the Study:
- To develop an efficient approach for mining periodic patterns from BSN data.
- To identify correlated periodic-frequent patterns, termed productive-associated periodic-frequent patterns, by employing a correlation test.
- To introduce an algorithm that discovers these productive-associated patterns to enhance healthcare diagnostics and patient care.
Main Methods:
- Mining periodic patterns from body sensor network (BSN) data.
- Applying a correlation test to identify associations between periodic patterns.
- Developing and implementing the PPFP-growth (Productive Periodic-Frequent Pattern-growth) algorithm to discover productive-associated periodic-frequent patterns.
Main Results:
- The PPFP-growth algorithm efficiently discovers productive-associated periodic-frequent patterns.
- The productiveness measure effectively filters out uncorrelated periodic items, highlighting significant correlations.
- Experimental evaluations on synthetic and real datasets demonstrate the algorithm's efficiency in revealing relevant correlated patterns from large datasets.
Conclusions:
- The proposed PPFP-growth algorithm offers an efficient method for analyzing BSN vital sign data.
- Identifying productive-associated periodic-frequent patterns improves the quality of diagnosis and treatment in smart healthcare.
- This approach is particularly beneficial for remote monitoring and care of elderly individuals in smart home settings.
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