Opportunity:

The global athletic footwear market size was valued at $64.3 billion in 2017 and the use of fitness apps has grown by 330% in the last 3 years. With fitness becoming an increasing part of a young professional’s life, finding ways to improve the efficiency of a workout or prevent injury are key to increased fitness success.

Solution:

Fun Run uses machine learning and sensors embedded in running shoes to track particular metrics and improve a runner/walker’s stride. In addition to tracking steps, heart rate (in the foot) and typical fitness tracking metrics that are currently available in the marketplace, Fun Run uses weight distribution to measure a user’s posture and stride. It also scans the running surface and analyzes data on both the surface type (soil, sand, concrete, etc.) and condition (dry, wet, icy, etc.). By continuously tracking and learning performance, the app can make recommendations based on a user’s goals. For example, if a user is concerned about hip pain, the app can let the user know if he or she is putting too much pressure on one side, then recommend relevant stretches/therapies and adjustments to the way they run in order to correct it. The algorithm can also use predictive analytics and alert people of the potential for injury or excessive soreness before people have experienced any pain.

Feasibility and Commercial Promise:

Athletes are increasingly interested in tracking their fitness. Of people who exercise at least monthly, 30% have a wearable fitness tracker and 29% use a mobile app to track fitness stats. Another ~25% plan to use these features in the future [1]. Despite significant commercial promise, the activewear market is saturating. 37% of fitness junkies state that brand is important, and 90% opt for high-end activewear brands [2]. As such, branding and high-performance will be integral to the success of Fun Run. Some of the features we plan to utilize may be expensive in the short term, such as sensors to analyze surface type and condition. The cost could be brought down by pulling in data from other sources (e.g., weather websites) or by relying on manual entry of some data.

Pilot:

A pilot would be rolled out at university track and field programs. We would work closely with a team of physicians and sports science staff at the University of Chicago (or comparable University), to certify and monitor all data and recommendations. We would seek to have each tailored recommendation and pain management solution be created and reviewed by our physician team to ensure proper legal protocol and medical viability.

Our pilot would seek out volunteer athletes who can provide large usage data sets (e.g., long distance runners) that would allow us to collect data on running strides, foot placement, weight pressure, shoe type and other relevant pieces of information. Additionally, we would bring the volunteers in for an initial screening process to create a baseline model of their postures, existing pain or medical issues, bone structure, etc. which would allow us to make better assessments from the running data we collect.

We would analyze the recommendations from our analytics software with our physician team to gauge relevance, accuracy, and benefit to the athlete above the normal utility of simple pain-based care.

Competitors/Risks:

A number of devices in the market measure biometrics such as heart rate and/or activity indicators such as steps and speed. They mainly allow people to track progress. Our product will be a leader in using AI to generate predictions based on such data combined with weather, running surface, and more granular biometrics information such as weight distribution. A competitive product on the market is Lifebeam VI, the self-proclaimed “first true AI personal trainer.” The product is a voice-activated Bluetooth biosensing headset with AI personal trainer. VI is focused on making you a proficient runner and is priced at $249.99. VI doesn’t analyze posture and weight distribution and doesn’t seem to be able to warn about potential injuries or excessive stress on your body. Other potential competitors are Google and Apple who can build on their personal assistants to also function as personal trainers. Existing user base and data will be a big advantage for them.

Sources:

[1] Mintel Report. Exercise Trends. US, October 2016.

[2] Mintel Report. Activewear. US, October 2016.

http://www.netimperative.com/2017/09/health-fitness-app-usage-grew-330-just-3-years/

https://www.engadget.com/2017/04/24/ai-personal-trainer-vi-headphones-running/

Team Members:

Sam Steiny

Rosie Newman

Gergana Kostadinova

Javier Rodriguez