How Peloton is using computer vision to strengthen workouts

by | Jul 29, 2022 | Technology

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As you do push-ups, squats or ab work, heft dumbbells, jump or stretch, a device on your TV follows you throughout your workout. 

You are tracked on your form, your completion of an exercise (or lack thereof); you receive recommendations on what cardio, bodyweight, strength training or yoga workout to do next; and you can work toward achievement badges. 

This is the next-level home fitness experience enabled by Peloton Guide, a camera-based, TV-mounted training device and system powered by computer vision, AI, advanced algorithms and synthetic data. 

Sanjay Nichani, leader of Peloton’s computer vision group, discussed the technology’s development — and ongoing enhancement — in a livestream this week at Transform 2022.

AI-driven motivation

Peloton Guide’s computer vision capability tracks members and recognizes their activity, giving them credit for completed movements, providing recommendations and real-time feedback. A “self mode” mechanism also allows users to pan and zoom their device to watch themselves on-screen and ensure they are exhibiting proper form. 

Nichani underscored the power of metric-driven accountability when it comes to fitness, saying that “insight and progress are very motivating.” 

Getting to the final Peloton Guide commercial product was an “iterative process,” he said. The initial goal of AI is to “bootstrap quickly” by sourcing small amounts of custom data and combining this with open-source data. 

Once a model is developed and deployed, detailed analysis, evaluation and telemetry are applied to improve the system continuously and make “focused enhancements,” said Nichani. 

The machine learning (ML) flywheel “all starts with data,” he said. Peloton developers used real data complemented by “a heavy dose of synthetic data,” crafting datasets using nomenclature specific to exercises and poses combined with appropriate reference materials. 

Development teams also appli …

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