Abstract
Increased awareness of the benefits of physical exercise has motivated people to improve physical fitness by doing high-intensity interval training (HIIT). HIIT (where one needs to work at 70-85% of one's maximum heart rate) and forceful exercise sessions can lead to health risks such as cardiac arrest, heat strokes, or lung diseases because people are unaware of their body health and endurance status. It is essential that the health parameters of people who exercise outside controlled environments like the gym be acquired and analyzed during workout sessions. This chapter aims to design an IoT-based timely warning system based on edge computing responsible for identifying unusual patterns in the monitored health parameters and alerting the person involved in an exercise about any deviation from expected behavior. The authors collect real-time data from individuals during the exercise sessions. The data analysis provides an assessment of the health parameters and predicts any health risks during the HIIT session.
TopBackground
During the HIIT workout session, the health parameters of a person change rapidly. If a person doing HIIT is unaware of their body health and endurance status— dizziness, cardiac arrest, and other health-related problems might increase. Therefore, there must be some real-time system that can alert a person during the HIIT workout session if their health parameters deviate from the expected behavior. If the health parameters of the people are not analyzed during their workout session, health-related problems may occur, which may also result in the death of a person.
There have been several works for monitoring the health of a person. Some of these works have analyzed the data in the cloud, which leads to an increase in the latency of the response time. There are other works that are monitoring the body parameters to optimize the performance of the sportsperson.
Smeaton et al. (2008) performs real-time monitoring of the health of sportsperson during their training and sporting activity. The authors use on-body sensors to monitor the motion, breathing, heart rate, heat flux, location, and galvanic skin response (GSR). The objective of this work is to maximize the performance of a sportsperson in their event. A web-based tool was used to visualize the aggregated sensor data. The authors optimize a sportsperson's performance by alerting the coach or user when the sportsperson reaches an excessive level of physiological response.
Santosh Kumar et al. (2014) have collected data like heart rate, weight, human BP, and movements from the athlete’s body. The authors provided an optimal solution using Wi-Fi to monitor the human BP and movements changing the human body with the help of sensors and displayed these values to LCD display. The objective of this work is to monitor the Blood pressure of the user during their sports activity to enable them to compare the abnormal performance of health indicators.
Key Terms in this Chapter
Machine Learning: A machine's capacity to reproduce intelligent human behavior is machine learning.
Sensors: Sensors are the devices used to collect data from things.
Edge Computing: Edge computing is a paradigm where data processing and storage are brought closer to data sources. This should increase response times while saving bandwidth.
IoT Analytics: Analytics for the Internet of Things (IoT) is a data analysis tool that evaluates the vast amount of data produced by IoT devices. IoT analytics analyses large amounts of data and generates relevant insights.
Artificial Neural Network (ANN): Artificial neural networks (ANNs) use learning algorithms that can modify or learn on their own when new information is received. As a result, they're an excellent tool for non-linear statistical data modeling.
Data Analytics: Analyzing, refining, manipulating, and modeling data to identify useful information, informing conclusions, and assisting in decision-making is data analysis.
Internet of Things (IoT): IoT refers to physical items equipped with sensors, computing power, software, and other technologies. It may communicate with other devices and systems over the Internet or other networks.