Emotional health: A data driven approach to understand our emotions and improve our health

Publication Date

8-1-2019

Document Type

Conference Proceeding

Publication Title

Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019

DOI

10.1109/CSE/EUC.2019.00071

First Page

339

Last Page

347

Abstract

Human emotions play an important role in everyone's daily life. People who have good emotional health pose to have good health. However, many things that happen in our life can disrupt our emotional health. These can lead to strong feelings of sadness, stress, or anxiety. In today's world many things that happen in our life can disrupt our emotional health. Emotions are also associated with problematic changes in the cardiovascular, hormonal and immune system that may lead to allergies, headache, migraine, Arthritis, blood pressure, diabetes and may penetrate its way to the heart which gives you heart attacks. In fact, recent studies show, for over 40 million people in the United States have been diagnosed with depression, the number of people coping (or trying to cope) with loneliness is unknown, and that feeling of not belonging is depressing all by itself. Emotions like anger, fear, Disgust triggers strong sensations in the upper body. Other emotions like depression, sadness and shame shows diminished sensation in the lower body, while some emotions combined both type of response. Though emptions are created in the brain, they can be manifest as physical feelings throughout the body. The most common way to understand human emotion is by capturing and understand signals provided by the human body. With help of IoT, NLP, Machine learning, Social Analytics and Artificial Intelligence we can capture and understand physiological signals and develop an emotion recognition system based on information provided by physiological signals.

Keywords

Amazon Alexa, Android, Apple Siri Kit, CEP, Complex Event Processing, Computer Vision, Decision ID3 Tree, EHR, Electronic Health Records, Health, Internet of Things, iOS, IoT, IoT architecture, Machine Learning, Microsoft Cortana, Mobile Sensors, Outpatient, Qualitative Metrics, Real time stream analytics, Sanjeevani Electronic Health Records, Voice Service APIs

Department

Computer Engineering

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