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Recent advances in machine learning technology have enabled highly portableand performant models for many common tasks, especially in image recognition.One emerging field, 3D human pose recognition extrapolated from video, has nowadvanced to the point of enabling real-time software applications with robustenough output to support downstream machine learning tasks. In this work wepropose a new machine learning pipeline and web interface that performs humanpose recognition on a live video feed to detect when common exercises areperformed and classify them accordingly. We present a model interface capableof webcam input with live display of classification results. Our maincontributions include a keypoint and time series based lightweight approach forclassifying a selected set of fitness exercises and a web-based softwareapplication for obtaining and visualizing the results in real time.

Muscle Vision: Real Time Keypoint Based Pose Classification of Physical Exercises