Preventing Treadmill Injuries with an Automated Stopping Mechanism

Jack Gallo explores the potential of computer vision to reduce treadmill-related injuries by automating the stopping mechanism, eliminating the need for manual safety clips. His project uses a model trained with Roboflow to detect when a runner's shoes or knees are no longer visible, triggering a stop event to prevent accidents. The system processes video footage into frames, runs inference, and generates an output video with a final STOP frame. Gallo discusses the methodology, including data collection, model training, and the application logic, noting that improvements in model accuracy and hardware setup are necessary for production readiness. The project demonstrates the broader applicability of computer vision in industries such as physical security, where it could be used to detect unauthorized presence and trigger alarms. Gallo credits Roboflow’s intuitive platform and resources, along with support from ChatGPT, for enabling him to develop the project without technical expertise, encouraging others to explore computer vision solutions for physical problems.