Abstract:

Today, IoT devices are becoming more and more popular. Smartwatches, smart bands, medical implants, traffic lights, or in-vehicle infotainment systems are only a narrow example of devices connected to the Internet of the current world. This article focuses on smartwatches and their ability to collect essential analytical data. Due to the COVID-19 pandemic, people use these devices primarily to monitor their current health status (e.g., pulse O2 level) or fitness and sport activity. At the same time, not many smartwatch users are aware that the data collected by their watches (e.g., GPS location, heart rate, or blood pressure) can be used by doctors to improve diagnostics. This research paper presents an example approach to detect health problems based on collected information from the smartwatch. The first section (Data Collection) covers the broad topic of collecting data from wearable devices. An application that acquires heart rate measurements has been implemented on the Samsung Galaxy Watch (TIZEN operating system). The next part focuses on data analysis. Possible solutions to detect human emotional states (e.g., aggression or fear) and potential health problems using heart rate are described. An approach using machine learning algorithms to detect anomalies and correctly classify human emotions is presented. Preliminary results demonstrate that even simple heart rate analysis, such as finding peaks, can be a good starting point for future research in healthcare.