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.


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.


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!

Seeed: NVIDIA Jetson Ecosystem Partner

Seeed is an Elite partner for edge AI in the NVIDIA Partner Network. Explore more carrier boards, full system devices, customization services, use cases, and developer tools on Seeed’s NVIDIA Jetson ecosystem page.

Join the forefront of AI innovation with us! Harness the power of cutting-edge hardware and technology to revolutionize the deployment of machine learning in the real world across industries. Be a part of our mission to provide developers and enterprises with the best ML solutions available. Check out our successful case study catalog to discover more edge AI possibilities!

Take the first step and send us an email at [email protected] to become a part of this exciting journey! 

Download our latest Jetson Catalog to find one option that suits you well. If you can’t find the off-the-shelf Jetson hardware solution for your needs, please check out our customization services, and submit a new product inquiry to us at [email protected] for evaluation.

About Author


September 2023