In this liveProject you’ll use human pose data and TensorFlow.JS’s PoseNet to build and train a machine learning model that can recognize workout exercises. This model will record and recognize the workout session of a user, to be logged for future comparison. You’ll need to prepare data structures for the TensorFlow.js Dataset API, and execute one-hot-encoding with a simple mapping function. You’ll also define model architecture using the TensorFlow.js sequential API, and train your model using the fitDataset method. You’ll finish up by saving your trained model in local browser storage to allow for model reuse.
This project is designed for learning purposes and is not a complete, production-ready application or solution.