Sleep Smart

We are developing a machine learning driven sleep optimization system that determines the optimal moment to wake users using real time physiological and behavioral data. Unlike traditional sleep trackers, our platform reduces sleep inertia by predicting sleep cycles and waking users during lighter stages of sleep. Our prototype integrates embedded sensing with intelligent modeling, using signals such as heart rate and movement to estimate sleep stage and calculate a sleep score in real time. This system continuously adapts to individual sleep patterns, with future development focused on a fully adaptive sleep coach that improves overall sleep quality and daily performance.

Sleep deprivation is a widespread and growing problem, with 36.8% of Americans reporting they do not get the recommended 7 hours of sleep, while the global sleep aid technology market is projected to grow from $103.5 billion in 2025 to $136 billion by 2030. Despite this demand, existing solutions, such as wearable devices and sleep tracking apps, primarily focus on collecting and visualizing sleep data rather than improving how users actually feel when they wake up. These products provide detailed insights into sleep duration and stages, but they stop at observation and do not actively optimize outcomes, leaving users to wake up groggy even after a “good” night of sleep. This highlights a clear gap in the competitive landscape: the lack of systems designed to optimize wake timing and reduce sleep inertia. Our approach directly addresses this by using a machine learning driven system that combines physiological signals such as heart rate and movement with behavioral inputs like caffeine intake, activity, and sleep history. These inputs are processed through a continuous modeling pipeline that performs sleep stage estimation and sleep cycle modeling to determine where a user is within their cycle and predict upcoming light sleep periods. A sleep score is continuously computed using features such as signal stability, sleep duration, and how optimal the wake moment is, allowing the system to select the optimal wake-up moment within a defined window. Instead of passively tracking sleep, this system actively makes decisions that improve how users feel and perform each day. From a feasibility and execution standpoint, this approach is highly practical because it leverages low cost embedded sensors and established signal processing methods, eliminating the need for complex infrastructure. The system can be built using existing hardware platforms and integrated with a mobile application, enabling rapid prototyping, iteration, and scalable deployment. An initial prototype is currently under development, with early testing underway using real sleep data to validate the system’s logic and technical approach. Over time, the system creates a strong barrier to entry through the collection of proprietary datasets specifically labeled around wake quality and the development of models optimized for wake time decision making rather than general health tracking. The business model is designed for scalability, combining a low cost wearable device with a subscription for continuous analysis, personalization, and adaptive coaching, which creates a recurring revenue stream while keeping the initial barrier to entry low. The initial target market will be students and young professionals, as both groups are highly affected by sleep deprivation and have a proven track record of spending money on wearable technology and performance enhancing tools, increasing likelihood of adoption and retention. This venture is further strengthened by my background in computer engineering with a focus on AI/ML and embedded systems, along with direct personal experience identifying this problem and building the initial prototype to solve it. Overall Sleep Smart creates a clear path towards a practical and scalable solution that will directly improve the way that people wake up.