The Challenge

Existing methods for identifying chronotypes based solely on sleep data lack accuracy, while traditional psychomotor tests for productivity assessment are inconvenient and time-consuming. Moreover, each individual’s response to caffeine, sleep quality, and physical exertion varies, even among those with the same chronotype. Lastly, the absence of suitable publicly available datasets made training machine learning models a challenge.

Our Solution

To overcome these hurdles, we created synthetic datasets for initial training of chronotype recognition models, productivity modeling, and personalized performance recommendations. Leveraging mathematical models derived from state-of-the-art scientific medical research, we refined the models using Reinforcement Learning. This allowed our algorithm to continually adapt to individual users’ unique chronotypes and productivity patterns, delivering highly accurate predictions and personalized suggestions.

The Outcome

With our solution, users can determine their chronotype through just 10-17 psychomotor tests conducted over 1-2 weeks. As they continue using the system, productivity prediction models stabilize and refine within a few weeks. Deployed on the IBM Watson cloud platform, our solution scales efficiently to accommodate a growing user base, making it adaptable for diverse applications and industries.

Data from synthetic datasets and scientific medical research drive precise, data-driven recommendations for enhanced effectiveness. With continuous learning capabilities and scalable cloud infrastructure, it sets new standards in performance enhancement, revolutionizing productivity management across professions and industries. Empowered by our solution, individuals can optimize their performance, boost productivity, and achieve their goals with unparalleled efficiency.