Smart sweat: Peloton’s AI is the future of home fitness

AI is driving the future of fitness, and companies like Peloton are leveraging the technology to enhance products and improve experiences for users. But what role will AI and data play in the future of at-home and connected fitness and how it will increasingly shape the landscape?

Peloton Guide (Peloton’s first connected strength device) is a good case study. It uses computer vision and machine learning technology to create focused and well-rounded training experiences from home. Guide’s Movement Tracker can recognize a user’s activity, encouraging and keeping them motivated to keep up with the Instructor’s cues.

AI is now a core tenant of Peloton and many other major home exercise brands. For insights into the future of smart connected home fitness (and some deeper understanding of just how embarrassed we’re going to feel at a machine’s consoling prods) I connected with Sanjay Nichani, Peloton’s VP of Artificial Intelligence and Computer Vision.

GN: Across the market where are we seeing AI intersect with fitness?

Sanjay Nichani: This is a great question because we’re seeing AI intersect with fitness more and more with consumers and their experiences. AI is something we’re continuing to tap into at Peloton and as we continue to conduct research, test products and speak with more people including our Members – we’re able to unlock additional ways that AI can improve the at-home fitness experience. Specifically, we see that AI can be used to drive convenience, accountability, motivation, education, gamification, competition, collaboration and social connection within the fitness market.

GN: Can you describe Peloton’s development history with AI? When did it first become a priority and how has it grown in importance?

Sanjay Nichani: Working with the latest technology is always top of mind for us because we’re always looking to enhance the Peloton experience. We’ve been working with AI for a few years now, starting of course with our class recommender system. You’ll really see AI take center stage with Peloton Guide since it is our first connected strength product. AI drives the experiences such as the Self Mode so that you can see yourself on the screen next to the instructor, Movement Tracker that gives your credit for following along to an instructor, and Body Activity that powers class recommendations to ensure you are working all muscle groups evenly. 

GN: How is machine vision aiding Peloton’s offerings? Can you explain what the Guide product is and how CV and ML help shape the user experience?

Sanjay Nichani: Peloton Guide connects to any TV to transform the biggest screen in any home into an interactive personal training studio. Once it’s connected, Members have access to Peloton’s world-class instructors who lead a wide range of fun-yet-intense classes and programs that use dumbbells and body-weight. Since Members and experts told us that they derive motivation from their metrics, we’re using AI for Guide’s Movement Tracker.

It’s really cool to see Guide’s Movement Tracker using Computer Vision activity recognition technology to recognize a Member’s activity as they follow along with the Instructors and complete each move throughout the class. For example, during a class, an Instructor will have a plan, where they’ll be coaching Members through different movements like bicep curls for 30 seconds or squats for 45 seconds. Guide recognizes the activity and metric-driven accountability to our members to keep them motivated to keep up with the Instructor’s cues. Additionally with Self Mode, Guide’s smart frame technology where the camera automatically pans and zooms on the Member working out, you can see yourself on screen and compare your form to the Instructor’s. 

Peloton Guide also shows members muscle groups they have recently worked on with a new feature called Body Activity. With this feature, Peloton will then recommend classes focused on the muscle groups that haven’t been trained in awhile to help Members have a more well-rounded training experience.

GN: Have there been any interesting learnings or insights from customer reactions? Have you changed course in any ways based on unexpected findings regarding user experience?

Sanjay Nichani: One interesting insight in various user and field testing trials was the value of having a bounding box around the person detected, this established a strong connection of the member to the Guide (proof that the Guide had detected the member, “seen them” and they were now locked. This simple visual feedback was far more valuable than perhaps displaying a skeletal pose, that was too busy and distracting, taking away from their exercise experience, or otherwise swinging to the other end, where nothing was displayed, which made users feel disconnected.) 

From the very beginning, data-driven insights have been baked into not only our company culture but into the products we produce. For example, our strength Members who are creating a gym experience from the comforts of their home without a human coach may not hold themselves accountable. The Members and experts we talk to often tell us that little feedback and motivation they get from the metrics is what keeps them going, e.g., you did x number last week and this week your number went up. This is exclusively a result of our cutting edge AI technology, Guide’s Movement Tracker. 

Our AI teams ensure that customer needs and feedback are woven into our product planning and assessment. We work with a number of other departments- Systems Engineering, UI/UX Design, User Research, QA, Field Testing – to ensure that the way AI is implemented within our offerings is directly addressing  the need our consumers. 

GN: What does the future of home fitness look like (for Peloton and beyond)? How are AI and MV helping shape that experience?

Sanjay Nichani: Honestly, we’re just scratching the surface of how AI  technology can impact fitness. 

Our AI teams ensure that customer needs and feedback are woven into our product planning and assessment. We work with a number of other departments- Systems Engineering, UI/UX Design, User Research, QA, Field Testing – to ensure that the way AI is implemented within our offerings is directly addressing  the need our consumers. 

We have a top notch cross-fucntional team  optimizing and diversifying our CV and ML tools to usher in new , safe and  fun ways to practice fitness. 

You can also see a future where CV and ML can help create more personalized content or offer real-time feedback. There’s a lot of potential with the technology and for Peloton, we’re going to continue experimenting. 

On day one Guide is going to provide a really different and motivating strength experience. But because Guide is because it’s built on CV and ML, we have an opportunity to keep iterating and making the product stronger, with more features, exercises and disciplines. We’re continuing to conduct field testing and have plans to keep updating Guide.