AI-Based Sleep and Circadian Rhythm Analysis Using Wearable Data

Sleep and daily activity patterns play a crucial role in determining how healthy a person feels, both physically and mentally. Modern life brings challenges like irregular work hours, shift work and travelling across the zones. These things can mess up the body internal clock known as circadian rhythm. In this study an artificial intelligence system was built to look at sleep and circadian rhythm. The system uses data from sensors that people wear, like fitness trackers, smart watches. The data included things like how active a person is, how well they sleep etc. A special kind of network called a Temporal Convolution Network is used. This kind of model is good at understanding patters over time long-term series of data. The model was trained to predict sleep quality and insomnia risk. To make the system more useful a simulation tool was added. This tool mimics real-life disruptions, like jet-lag, working night shifts. It helps show how these events affect sleep and activity rhythm. The simulated outputs were quantitatively and qualitatively validated against real world wearable datasets, demonstrating high consistency and generalization capability of the proposed approach. Furthermore, the system was optimized for Edge AI deployment, enabling low-latency, on-device inference while ensuring data privacy and reduced dependency on cloud-based computation. The experimental results indicate that the proposed system effectively delivers predictive and interpretable insights for sleep health assessment, circadian behavior analysis and preventive healthcare applications, thereby establishing its potential as a scalable and real-world deployable solution.