Stress Detection Using Machine Learning Techniques in IoT Environment

Stress Detection Using Machine Learning Techniques in IoT Environment

Dimple Chehal, Parul Gupta, Payal Gulati, Rosy Madaan, Komal Kumar Bhatia
Copyright: © 2022 |Pages: 14
DOI: 10.4018/978-1-6684-2508-4.ch004
OnDemand:
(Individual Chapters)
Available
$33.75
List Price: $37.50
10% Discount:-$3.75
TOTAL SAVINGS: $3.75

Abstract

Employment of various sensors used in IoT can help create a sustainable urban life. Rapid advancements in IoT have made human existence smarter. Smartness may be associated to office, home, networks, energy consumption, agriculture, education, retail, and even healthcare. Smart health management addresses its populations, such as tracking routine activities, obesity, nutrition intake, heart rate, glucose level, oxygen level, body temperature, or even stress level monitoring. Stress is a condition which is being faced by people irrespective of their age, gender, or profession. But its identification at an early stage can help in preventing the consequences. This work presents nine machine learning techniques for identifying stress using the SWELL dataset. Hence, automated classifiers were utilised to predict working circumstances and stress-related mental states and were compared for accuracy for three stress conditions (no stress, interruption, and time pressure).
Chapter Preview
Top

1. Introduction

The usage of various Internet of Things (IoT) sensors can lead to a sustainable urban environment, thereby improving the quality of life of citizens of a smart city (Muhammad et al., 2021). Smart healthcare constitutes one of the important aspects of a smart city. Smart Healthcare with IoT applications can be enhanced with the usage of sensors and actuators in patients for monitoring and tracking patients’ health and medicines. The data generated by the sensors and actuators in a smart city, upon analysis lead to crucial data insights which can then be utilized to improve any smart city’s efficiency and effectiveness. The initial step for the development of any smart city is data analytics. After the collection of a smart city’s data through sources such as sensors, smart phones, citizen repositories, city repositories, analytical reasoning can be performed so as to generate the necessary information for good governance and decision making. The integration of IoT and Machine Learning monitoring the patients has drastically improved customised healthcare by enhancing patient care quality while decreasing costs. The Telehealth technologies are seen to be important in resolving substantial challenges for monitoring patients remotely. The key advantages are constant monitoring of a patient’s health irrespective of the patient's site, greater accessibility to healthcare, lower healthcare costs, and improved treatment quality. With the advancement of technology, new IoT-based solutions for the collection of data and its analysis have been developed. The data can be collected from different areas within the smart city such as health, education, occupation, energy, weather, pollution, etc. Smart health management tackles both healthy and unhealthy populations, such as monitoring senior or younger people's daily tasks, obesity tracking, nutrition-intake tracking, heart rate, glucose level, oxygen level, body temperature, and even stress level tracking. Stress is a broad phrase that encompasses the psychological and biological activities that occur during emotionally and intellectually challenging situations. It is a disease that affects everyone, regardless of age, gender, or career. Stress leads to the altercation of thermal, acoustic, optical, impedance and electrical signals. As per American Institute of Stress, 80 percent of workers feel stress due to workplace, 50 percent require assistance to learn to handle stress, while 42 percent believe that their colleagues need it. As per the Health and Safety Executive (HSE), stress related to work, anxiety or depression contribute to 44 percent of all sick health cases related to work, further 54 percent of the workforce duration was missed due to illness in 2018-19 (Bobade & Vani, 2020). Stress has been linked to immune system breakdown and an elevated risk of cancer. These figures, as well as the repercussions of stress on humans, need the development of a system capable of recognising stress conditions and treating stress through personalised actions or, if required, medications. The ongoing Covid-19 outbreak has further exacerbated stressful situations across the globe. All people irrespective of their age, gender or profession are facing this condition. It should be dealt on a daily basis lest it endangers our mental sanity and routine work. . But, its identification at an early stage can help in preventing other repercussions that follow. The change in bio signals can help in detection of stress amongst humans. The combined effect of IoT for sustainable healthcare and big data analytics helps to increase medical facilities efficiency by recognising the risk factors, providing diagnosis and the treatment process. It not only meets the hospital management examination index need, but it also achieves superior cost growth between treatment and care services.

Complete Chapter List

Search this Book:
Reset