Real-Time Exercise Tracking Using Computer Vision: A Novel Approach for Dumbbell and Stand-Sit Exercise Monitoring
Shivani Sharma, Shreya Pandey , Suhani Gautam
Computer vision, Exercise tracking, MediaPipe, Pose estimation, Dumbbell exercises, Stand-sit movements, Real-time feedback, Fitness technology, Non-intrusive monitoring, Wearable alternatives.
This paper presents a novel real-time exercise tracking system designed to accurately monitor and count dumbbell exercises and stand-sit movements using computer vision techniques. Utilizing the advanced capabilities of the MediaPipe library, the system provides immediate feedback, promoting proper exercise form and reducing injury risk. This work addresses the limitations of traditional wearable-based systems and highlights the potential for computer vision in fitness applications. Traditional exercise tracking systems often rely on wearable devices, such as fitness bands or smartwatches, which use accelerometers and gyroscopes to detect and measure movements. While these systems offer a degree of convenience and portability, they are limited by factors such as sensor placement, calibration issues, and the inability to capture complex movements accurately. Additionally, wearables can sometimes be intrusive or uncomfortable during exercise, potentially impacting the user experience and the accuracy of the data collected.
Article Details
Unique Paper ID: 165006

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 2904 - 2908
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