  Recent advances in machine learning technology have enabled highly portable
and performant models for many common tasks, especially in image recognition.
One emerging field, 3D human pose recognition extrapolated from video, has now
advanced to the point of enabling real-time software applications with robust
enough output to support downstream machine learning tasks. In this work we
propose a new machine learning pipeline and web interface that performs human
pose recognition on a live video feed to detect when common exercises are
performed and classify them accordingly. We present a model interface capable
of webcam input with live display of classification results. Our main
contributions include a keypoint and time series based lightweight approach for
classifying a selected set of fitness exercises and a web-based software
application for obtaining and visualizing the results in real time.
