Fiber-Optic Signal Based Deep Learning for Sleep Medicine Research

As living standards continue to improve, people are becoming increasingly concerned about their own health and the quality of their sleep. However, currently available sleep monitoring devices often have issues with uncomfortable wear and low data accuracy, which limit users’ experience and the effectiveness of data analysis. At the same time, there is increasing attention to real-time monitoring technology based on human physiological indicators in response to the demand for personalized healthcare management. In this context, fiber optic signal technology has emerged. By utilizing fiber optic sensing technology, it is possible to non-invasively acquire subtle human signals, such as respiratory rate and heart rate. This paper proposes an algorithm combining microprocessor and machine learning technology, which can effectively identify the user’s sleep state and Physical change, and improve the accuracy of prediction.