In this liveProject, you’ll test the functionality of a TensforFlow.js based fitness assistant and assess its capabilities of pose estimation for workout sessions. You’ll move data from a PoseNet model to a TensorFlow.js inference function that can recognize a workout type and add delays to prevent unnecessary logging duplication. You’ll then utilize TensorFlow.js’s prediction functionalities to get prediction results for your recognized workouts, pick top scores, and return the recognized workout type. Finally, you’ll use Material UI and React.JS to display a complete workout history to the user.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
When you start your liveProject, you get full access to the following books for 90 days.
- Basics of HTML
- Basics of Node.js, NPM, and Yarn
- Basics of React
- Basics of TensorFlow.js
- Visual Studio Code
- Basics of machine learning
you will learn
- Reusing code for PoseNet generated human pose data collection
- Running the inference and getting top result by classification score
- Updating data stored in local storage with info about workout type
- Adding solution to prevent result data noise
- Implementing dialog to display workout data history
- Implementing logic to reset the data
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
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- Compare with others
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- book resources
- Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.