Monitoring and diagnosis of the well-being with biosensors and intelligent systems

Abstract

Nowadays, society is more aware about their wellbeing and health, making wearable devices an unexploited and affordable way to continuously monitor them. Smartwatches have gained popularity among wearable devices, enabling access to daily vital physiological measurements, which help people be aware of their health condition. Offering non-invasive, real-time daily monitoring,providing health-related data that may be used to identify a lack of stability in the body, whether it is physical or mental. This project introduces LM Research, a smart monitoring system consisted mainly of a webpage, REST APIs, machine learning algorithms and smartwatches. This system monitors users’ physical and mental indicators to prevent a potential well-being crisis. This will be accomplished by collecting psychological parameters in smartwatches and mental health data using a psychological questionnaire to further develop a supervised machine learning well-being model that will forecast smartwatch users’ well-being. The use of sensors in smartwatches provides an accurate measure of physiological functions of the body; for this reason, a well-established Brand (Garmin) was selected due to its high-quality sensors, which provide more accurate data in contrast with more economical alternatives. This research focuses on determining the most important physical and personal parameters that impact a person’s well-being by feature selection, which will be fed to the machine learning forecasting model. To engage with users and acquire all the data needed to predict their well-being, a website was built and housed in the cloud, allowing the creation of a larger and reachable dataset. In contrast to building the project in a local computing environment, which has more constraints such as data storage and processing, cloud computing makes it scalable, flexible and mobile due to using external servers’ capability.