Senior Wellness Analysis

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Abstract — Because of the increased lifespan, there is a increasing demand on prevention of disease for senior wellness.
Wellness means not just free of disease, but also includes wellbeing and happiness in physical, mental, emotional, and environmental components. For monitoring senior wellness status, biosensors such as Electroencephalography (EEG),
Electrocardiography (ECG), oxygen saturation (SpO2), blood pressure (BP), and respiration rate (RR) sensors and environmental sensors (temperature, humidity, motion, and light sensors) were used for sensing data collection. Sensing data from bio- and environmental sensors are transferred to smart gateway in smart home and
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Sensing data is extracted from smart home database and transformed for the analysis and decision making. In this paper, we develop an EM-based inspection service middleware for monitoring elderly wellness status based on time, situation and zone transition. This inspection service middleware for the prediction of abnormal health status has three steps as follows; monitoring, Index assessment, risk assessment and decision-making. The index assessment step used fuzzy logic, the risk assessment step uses the decision-tree model for the…show more content…
All these heterogeneous factors affect the integration of data from different sensors measuring the same phenomena. Also, more and more, heterogeneous sensors are linked together to build sensor networks through internet.
In this paper, we introduce a multi-level assessment model for monitoring elder’s health condition that can be used for the prediction of abnormal health status using multimodal bio-sensors in smart home environment. This monitoring model considers biometrics data as well as the environmental sensor data and these measurements are used for the bio-index generation for prediction of senior health status. This multi-level assessment model has five modules as follows: (1) monitoring biometrics data (body temperature, EEG, ECG, respiration rates, SpO2 and blood pressure) and environmental sensor data (location, time, weather, temperature and humidity) (2) index assessment from measurements using
SVM, (3) risk assessment and rule generation using

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