Smart Wearable Devices for Prediction Using Machine Learning Techniques: Depth Exploration

The emergence of the COVID-19 epidemic has emphasized the continuous need for innovative and discreet methods to monitor and evaluate the progression of the disease. Wearable technology, equipped with an array of sensors and powered by advanced machine learning algorithms, presents a highly promising solution. These devices make use of sensors like heart rate monitors, pulse oximeters, temperature sensors, and accelerometers to continuously gather real-time data. This research explores how wearables, including fitness trackers and smartwatches, can be used to continuously monitor the COVID-19 disease and capture the data. It also explores applications in assessing COVID-19 severity through the utilization of machine learning techniques. Machine learning techniques including decision trees, support vector machines, and deep neural networks are used to process this data. This includes the early detection of warning signs, evaluating disease progression, and distinguishing between mild, moderate, and severe cases. The integration of patient-reported symptoms with wearable data further enhances the precision of assessing COVID-19 severity.