A Comprehensive Study of Machine Learning-Based Improvements in Sleep Monitoring Wearables

Wearable medical devices are designed to monitor and gather health-related data when worn. Their purpose is to improve the lives of individuals and their healthcare teams by delivering valuable information about their health condition, which may help in making intelligent choices about treatment. The integration of artificial intelligence (AI) and machine learning (ML) further amplifies these capabilities. AI encompasses the creation and implementation of computer systems that can do functions often associated with human intelligence such as generating algorithms and models that enable computers to evaluate data, engage in logical thinking, acquire knowledge, and make judgments or forecasts. Machine learning, an essential component of artificial intelligence, entails the process of training algorithms using data to identify patterns and generate predictions or classifications without the need for explicit programming. Machine learning models enhance their performance and adjust to new input through iterative learning processes. Although the exploration of AI and machine learning in wearable medical devices is still in its early stages, it is clear that these components provide a wide array of advantages. However, it is essential to recognize that they also provide distinct difficulties and factors that demand meticulous study.