Data mining for autonomous wearable sensors used for elderly healthcare monitoring

The paper presents some aspects regarding data mining used modeling and prediction of the patients' health
state parameters.
The proposed wearable device integrated by using wireless personal networks (WPNs) can sense, process and
communicate vital signs through internet for healthcare monitoring. These WPNs are fitted for medical
applications and offer continuous ambulatory health monitoring by using non-invasive methods. Generally, the
body sensor network (BSN) for medical applications are based on big data fusion and cloud computing
technologies (PaaS, SaaS - for data storage and sharing solutions).
The big data fusion includes preprocessing (filter the noise), feature extraction (data abstraction), data fusion
computation (modeling different information type and fusion), and data compression (reducing the information
stored in memory and transmitted by the transceiver).
The fusion between wearable wireless body sensor network (WWBSN), IoT and Cloud Computing will allow
doctors, emergency stations or caregivers to track and receive data from BSNs about patients in different places.
By using biomedical sensors can be studied the human behavior and physiology, the body's response
physiologically and emotionally to various physical and mental diseases. The WWBSN can cover monitoring for
cardiovascular, diabetic problems or mental disorders (Alzheimer).