Tribe: The “Pokemon Go” of fitness (Team Awesome)

  • Genesis and Overview

Our team initially did a review of Pokemon Go’s success as an augmented reality platform. The game was superimposed over a layout of a real-world location on a user’s cellphone, and would interact through augmented reality. The game was widely popular and one of its unintended side-effects was that it motivated a large number of players to be more physically active. As each of the different goals were in different locations, players had to walk significant distances if they were particularly competitive. While not the primary purpose of the game, it got us thinking that there was definitely an angle to be tapped on.

With the increased adoption of wearables, the cult-like fitness industry is ripe for an AI integration. Tribe is our vision of an adaptive workout platform that combines machine learning and augmented reality. Tribe knows about all your physical attributes, such as height, weight, how much you ate yesterday, how much you slept and even what your fitness goals are. Based on all these, it can propose a workout for you that’s tailored to what your body needs at that very point in time. Each person will have a completely unique interaction with Tribe.

  • The Problem

There are a whole range of fitness apps on the market now that attempt to guide users towards individual fitness goals. Most of them are fairly rigid and expect you to follow a set training program without any deviation. The classic couch-to-5k running guide is a clear example of this, where you can’t skip or modify a day’s workouts. Given the rigidity, it’s also not surprising that the attrition rate from these programs are remarkably high.

There are several solutions to the individual who desires varied, customized workouts.  seemingly simple solution to the individual that wants a customized, flexible workout, is to hire a personal trainer. However, that is a costly route which wouldn’t work for most people. To figure out how our platform could help, we tried to understand exactly what a personal trainer does, and how it could be replaced by AI. A personal trainer is usually well educated with a substantial background in kinesiology. They’re training style has also evolved based on the number of clients they’ve seen, and their ability to prevent injury before it happens, as well as understanding each client’s limits, and trying to push them farther. In essence, it was a skill that was based on interpretation after being experienced with multiple repetitions – sounds a lot like machine learning.

  • The Solution

Enter Tribe, a software based solution that provides the first-ever augmented reality workout. Tribe will leverage a system of virtual gyms similar to Poke gyms with particular workout functions at each gym. This means that as the user base grows, groups will exist at certain gym locations and users will be performing the same exercises simultaneously. While this is not a group workout, there is a social component to it. Members, in a sense, are part of a workout tribe, similar to those that have been popping up around the globe in the social fitness craze of the last decade.

App-based fitness challenges are still virtual. One of the most well known virtual experiences is Fitbit’s “Adventures.” Through adventures, users can take a virtual journey through a wilderness hike, motivating them to reach a step goal. The experience is based on images, and there is no integration with the user’s surrounding environment.

  • How it works

Step 1 – Collecting User Data. Users sign up to the platform and integrate it with their existing fitness tracker. For the purpose of this illustration, we’ll assume that Tribe is synced to a Fitbit. The user is then able to confirm the height and weight inputs that are retrieved from Fitbit as well as add their own personal information such as location, workout preferences and any past injuries. Based on all the information provided, Tribe is able to access historical fitness data from the Fitbit (heartrate, step count, workout intensity), and combine this with the workout goals to create a custom workout plan for the user.

Step 2- Location recommendation. When a user wants to have a workout, they can access Tribe and let it know that they’re trying to spend a given time on a workout – 60 min for example. Tribe will then process where you are, as well as the workout you need and give you directions to a workout location. Users will be able to elect from free locations (a public fitness area or an open field), or paid locations (a gym that Tribe has partnered with). The prompt to go to the location will be very similar to that of PokemonGo which engages the user in a game-like environment using augmented reality.

Step 3 – Workout Recommendation. Once you’re at the location, Tribe’s guided workout begins. This is where the platform’s core competency is. Tribe will already have all your data from the current day, and even the day before. It’ll be able to understand that you may have had a long night out with little sleep, or that you haven’t worked out in 4 days and have been sleeping 12 hours a day. Based on this information, as well as your physical attributes and goals, Tribe will be able to propose a workout that’s customized for you at that very point in time.

Step 4 – The Guided Workout. Very much like PokemonGo, Tribe will be able to guide you through your workout through augmented reality. Users will be able to place their phone in a visible place and see objectives on the screen. Perhaps an animated balloon 4 feet off the ground as a target for box jumps, or a cartoon character that holds a 2-minute plank with you. In addition to all this, the use of the front and back facing cameras on the phone will keep users honest by tracking their movements – very much like a more modern, portable version of Dance Dance Revolution.

  • Challenges and proposed alterations

One of the challenges for this app is that unlike Pokemon Go, there will need to be workout specific spaces that account for the safety of users. A gym cannot exist in the middle of an intersection, for example. An exercise that requires the user to impact the ground cannot be on concrete.

  • Marketing and partnerships

Some obvious marketing opportunities exist, starting with existing gyms and personal trainers. As the workouts could be anywhere, one potential location is existing gyms. Additionally, the trainers at the gym or independent trainers could craft workouts that can be integrated into the app and advertised to its users. Our target customer would already be interested in working out, and therefore our marketing campaigns will be targeted at the places they already are – in addition to gyms, also 5k races, farmers markets, and healthy food stores where athletes looking for more personalized workouts congregate. Lastly, we will use social media, particularly fitness influencers, to gain traction.

  • Funding Requirement and Timeline

While we will need $2M to fully develop this technology, we are asking for $200K in funding to get us to a first pilot in one mid-sized city. This will enable us to create the initial exercise programming prototypes, develop the initial software prototype, identify locations within a city for gym activities, and partner with local organizations to gain a following. Once we can prove the model in a single city, we will expand to additional geographies.


Pokemon Go: Augmented Reality

The problem:

Virtual reality, for all its advocates has one clearly apparent shortcoming: the absence of integration with daily human behavior. A virtual world is one separate from the one we physically live in. Augmented Reality, on the other hand, uses technology to connect us with real world experiences. A prime example of this is Pokemon Go, which launched the first successful AR-based gaming app in 2016. In this post, we will examine Pokemon Go as a success case for integrating AR into our entertainment seamlessly.


Pokemon Go is a game in which characters are overlaid on the real built environment throughout the world, featured in areas of note or importance. The primary purpose of the game is to obtain a list of different pokemon monsters, all of which are located in different places. Along the way you have to recharge, and hunt for monsters that rank in rarity or availability and location.

The game overlays a virtual interface to the real world map on your cellphone and can sense your habits and project different goals and objectives depending on the user.


The game is highly dependent on user network effects to determine which characters appear in different locations. It does this by running algorithms on desired  or “rare” characters and then featuring them for brief moments in uncommon locations. The users then communicate with each other through the platform to share news of each rare sighting to and eventually congregate in the same area.

Using AR, the game has enabled users to connect to a virtual reality world and incentivises activity which has then been used to relate back to the real world. A clear example of this would be businesses that pay a fee to be listed as a “pokestop” where game players can recharge their lives, and in doing so patronise the store. This has taken marketing to a whole different level and created a separate platform for businesses to target the user demographic that play the game.

The game has also been able to simultaneously overlay reality with virtual reality without the use of any special hardware (like a VR headset or console). In doing so, an artificial environment has been created on the users mobile phone, surpassing the multiple levels of stimulus that you would receive from the real world image presented. This offers the ability for this sort of AR platform to engage users through a myriad outreach mechanisms extremely effectively.

The Future of AR

Pokemon Go is an example of how effective blending the physical and virtual worlds can be in a user experience. This was the first time AR was brought to a mass audience. The rapid adoption and wild success of the game shows that AR is very much technology that’s here to stay, and can be implemented on existing platforms without too much effort. This opens the door to multiple future uses, beyond chasing a yellow cartoon character around town.


Team Awesome

Instituting an Ethics Framework for AI

The Problem

The progresses in Artificial Intelligence (AI) in recent years, from data mining to computer vision and from natural language processing to robotics, have demanded people to start think about morality’s importance and complexity in the design of AI. According to the Economist almost half of all jobs could be automated by computers within two decades.[i] Many of these jobs are complex and involve judgements that lead to significant consequences.

The dilemma self-driving cars faces is a good example. In the situation of a brake failure, a self-driving car can either keep going straight, which would result in the death of pedestrians, or swerving to avoid hitting the pedestrians, which would result in the death of dogs crossing the street. How should the AI be programed to make decisions for the self-driving car in situations like this? Drones used by military to target and suppress terrorists is another well-debated example of the importance of morality in the design of AI. According to the New York Times, the Pentagon has put AI at the center of its strategy to maintain the United States’ position as the world’s dominant military power.[ii] The new weapons would offer speed and precision unmatched by any human while reducing the number — and cost — of soldiers and pilots exposed to potential death and dismemberment in battle. How do we make sure these drones make the moral decision in the battlefield?

As innovation in AI accelerates, we need to get ahead of the curve and implements morality into the design of AI so that gains today are not taken at the cost of future abatement. We should define morality before an AI does.

Potential Challenges

The major challenge of programing ethics into AI is the fact that human ethical standards are currently imperfectly codified in law and they make all kinds of assumptions that are difficult to make. Machine ethics can be corrupted, even by programmers with the best of intentions. For example, the algorithm operating a self-driving car can be programed to adjust the buffering space it assigns to pedestrians in different districts based on monetary amount of settlement of previous accidents in each district. The assumption is that the bigger buffering space in districts with higher settlement costs can reduce the potential for higher settlement. The assumption seems reasonable, but it is possible that the lower settlements in certain districts were due to the lack of access to legal resources for residents of poorer neighborhood. Therefore, the algorithm could potentially disadvantage these residents based on their income.

The Solution

We have established the need to teach AI to have a learned concept of morality. However, given the challenges, governments and regulators need to be lead the effort to establish a globalized standard for machine ethics. These standards need to be clearly instituted and codified by legislature. However, governments’ lack the resources and talent in the field of AI would require them to have private sector’s involvement. At the same time, companies that are already developing products using AI such as self-driving cars have conflict of interests in assisting the government. This poses a potential opportunity for us. We can build a product that crowd-sources human opinions on how machines should make decisions when faced with moral dilemmas to help governments write these legislatures. For example, we could crowd source scenarios and the most appropriate responses to those scenarios for self-driving cars on our platform, and then contract with the governments to evaluate our findings and implement them into algorithms of self-driving cars looking to enter the market. Although sales process to government entities can be lengthy, our role as a third party between the regulator and the companies developing AI products and the potential multi-year revenue stream with a mandated project place us in a very good position.




Team Awesome Members:

Rachel Chamberlain

Joseph Gnanapragasam

Cen Qian

Allison Weil