Computer Vision with Fitness: Effectively Evaluate Human Motion with Feedback in Real-time
Hardware: NVIDIA Jetson Orin Nano
Application: Pose Estimation for Gym Training
Industry: Intelligent Fitness Evaluation
Deployment Location: US, Spain, France
In today’s fast-paced world, fitness enthusiasts and athletes are constantly seeking innovative ways to enhance their training routines. It’s getting more critical to learn precise and real-time feedback during gym workouts. Traditional methods of assessing exercise form and posture often fall short in providing instantaneous insights, always coming with the bias that may be attached to the coach’s insight, which will not lead to more targeted and accurate analysis results and guidance.
Challenge
The challenges that fitness trainers and enthusiasts face when analyzing real-time workout progress, particularly in assessing exercise form and correctness, are multifaceted. Traditional methods often rely on the naked eye, which can be subjective and prone to human error. Furthermore, providing instant feedback on form deviations or improper technique during a workout session is practically impossible for a single trainer overseeing multiple clients. We need to figure out how to offer a consistent, objective, and real-time pose estimation analysis through video recording or live streaming.
Solution
NVIDIA Jetson Orin Nano can tackle the challenges of real-time workout progress analysis and exercise correctness with computer vision capability. It utilizes the YOLOv8-s pose model and NVIDIA TensorRT to process and inference high-resolution 640×640 input images sourced from IP Cameras. The system shows remarkable inferencing performance, clocking in at 106 FPS. At this core, the YOLOv8-s pose model, trained on the COCO-pose dataset, stands as the foundation to provide unparalleled accuracy in human motion tracking.
By leveraging body motion data points, it accurately identifies and analyzes users’ precise movements, offering invaluable insights into exercise form and posture. Moreover, it goes beyond mere observation, automatically counting exercise repetitions and recognizing the specific training type being performed. This innovative fusion of hardware and software not only empowers trainers with real-time feedback but also enables them to tailor their guidance, ensuring clients perform exercises correctly and efficiently, ultimately driving better fitness outcomes and reducing the risk of injuries.
Check out this Github repo to try the exercise counter demo now!
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