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Using AI to channel Olympic excitement into skill development of homegrown athletes
This talk explores using AI and computer vision to analyze speedskater form, provide real-time feedback, and connect athletes with Olympic performance data for skill development.
I coach at a speedskating club, and one of the most difficult aspects of training young athletes is focusing them on technical aspects, tracking progress, and communicating tips and form correction effectively. This is a mobile app designed specifically with speedskaters in mind, and it gamifies the process of holding a “basic position” i.e. the crouched-over skating position.
The user begins a game and video is captured. A wireframe is overlaid on the athlete, and the knee and hip angles are calculated. When the athlete goes into basic position, the user enters a session and begins to accrue a score. Score accrual multipliers are applied the closer to “perfect form” an athlete has, which has visual indicators displayed in realtime. Feedback from an AI coach is also given in realtime, which keeps the user engaged and focused. The user finishes a session after they stand up, and a report generated by AI is shown to help them understand what could be improved, as well as their final score.
AI also uses the user’s home country to query a public API on speedskater results and world records (ex. a Canadian user would be shown world records and stats of famous skaters such as Laurent Dubreuil). It helps users connect more with how strong the Olympic-level athletes are, along with recording progress and showing improvement over time.