Smart Gym was a project that our team worked on as a part of UW-Madison's IoT Lab. The idea behind smart gym was to retrofit gym equipment with sensors that could track user's workouts and transmit the data to their phones.
I have always taken fitness seriously and my time in weight rooms has drawn my attention to a number of problems that are present in the space. I tried to solve some of these problems with a previous project 'Weight Your Turn'. The IoT lab offered an opportunity to revisit these problems and tackle them with new technologies. There were a number of problems we could have tackled but I focused our attention on 2 of them:
- Allowing users to track and record workouts seamlessly (aka without a pen and paper)
- Eliminate the need for high-cost wearable technologies
As a weight lifter I always became frustrated when I attempted to to track my workouts. My options were to either carry a pad & paper to write everything down or manually input it into my phone. The only solutions available on the market were high-cost wearable technologies with limited accuracy.
The solution we landed on was to retrofit gym equipment with tracking devices and then transmit the information to users' cell phones. Initially we avoided free weights and focused on equipment with adjustable weight stacks (about 50-65% of equipment in most gyms). The tracking devices could measure the amount of reps at certain weight a user completed for each machine. We then used bluetooth to transmit the data directly to the user's phone to view on our Smart Gym mobile app.
The technology in the device was pretty straight forward: an Arduino Yun microcontroller with Bluetooth dongle coupled with a pressure sensor and triple-axis accelerometer. We enclosed the hardware in a 3D printed case, and added a RGB LED and start button for testing purposes.
Parse was used as the backend for storing all user data as well as information collected from the Arduino. We then used Ionic, a hybrid application framework, to develop a cross-platform application. The plan was for the app to be free for users.
The central concept of the business model was to target the gym owners and not the gym members. The gyms would pay an upfront fee for installation of the devices on their equipment and then recurring subscription fees to support the software at their gym. The owners' incentive for purchasing the Smart Gym service is that it would give them a differentiator in a competitive market with few ways to separate. Members of that gym would be motivated to join for our unique service they could not receive elsewhere. Members would also be inclined to use the service since the barriers would be low with them not having to purchase equipment or pay for the app.
Overall I felt that Smart Gym was an enriching experience that allowed us to explore a number of new technologies in the IoT space. However, the project had a few glaring problems:
- Devices: accuracy problems on top of the fact that we struggled to find ways to power them and make them less invasive.
- Legal: retrofitting gym equipment can actually be a gray area. Since most gyms actually lease equipment, in some cases we would have had to get permission from the equipment manufacturers.
- Pain point: the truth is for most gym goers this problem just isn't strong enough to warrant a new solution. In reality this service would be better suited for niche markets like athletic teams.
Apart from these glaring problems with the concept, I also walked away with a few other takeaways:
- Hardware startups are tricky. The moment a company incorporates hardware into their model it creates a new bag of problems & the costs instantly multiply.
- Using existing technologies vs creating your own. There are companies that focus on certain aspects of hardware or software so in some cases you are better off just using pre-built solutions. However, the flip-side of this option is that your IP "moat" becomes a lot narrower.
- In IoT organized data is king. After seeing numerous projects in the IoT lab one thing that stood out to me was that the end goal was almost always to end up with a set of organized data points that could be used to drive decisions. The hardware/software involved was really just a means to an end.