NAVIgating our story

Navi is a mobile app that uses evidence-based science to aggregate health metrics into one easily understood score. It then uses machine learning and predictive analytics to determine how users should improve their metrics in order to reach their goals, whether they are losing weight or increasing energy.

Navi was born in a fit of frustration during one of many health tracking device demos at CES a year ago. Despite being surrounded by fitbit, iHealth, and similar trackers, the promise of more intelligent devices was unfulfilled.

Current players in the health tracking space lack the biological sophistication and creative naivety to apply machine learning in a way that goes beyond feature creation. The fitbit allows users to measure steps, or sleep, but doesn't tell the user how sleeping less might be causing them to lead a sedentary life. And more importantly, the fitbit doesn't learn.

Ultimately, Navi simplifies and aggregates health data streams for users to help them reach their goals and learns about them as they use the app more in their everyday lives.

Target User

Navi is built for the everyday user who tracks their health metrics but is tired of the diminishing value they receive from their data. Whether it is through wearables, self-input, or HealthKit on ios8, users can makes sense of and interact with the massive amounts of data they are collecting.

The Product

Intended Features

  1. A single health score (Navi Score) determined through algorithms that factor in BMI, steps walked, hours slept, blood pressure, heart rate, and more.
  2. Continuous updating of data metric integration into the Navi Score as new hardware and software are released to track more data
  3. User can set goals for what they want to achieve (lose weight, relax, etc.) Navi then determines a personalized profile for how the user should change their metrics (sleep more, walk less) to reach that goal
  4. Time relative push notifications are sent to remind user to eat more fiber, sleep, etc.
  5. Recursive feedback and machine learning are used to increase the accuracy and efficacy of predictive capabilities around each user's Navi Score.

Demo Features

  • Data stream aggregation
  • Navi Score profile
  • Data correlation algorithms
  • User Goal profiles

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