Create Your Own Personal AI Trainer

Table of Contents

1. Introduction
2. Creating a Personal AI Trainer
3. Finding Points and Angles
4. Angles and Gestures
5. Creating Real Computer Vision Apps
6. Setting up the Project in PyCharm
7. Using the Pose Estimation Module
8. Creating the AI Trainer
9. Finding Angles Between Landmarks
10. Counting Curls with Gesture Detection

Creating a Personal AI Trainer: Counting Curls with Pose Estimation

In this tutorial, we will be creating a personal AI trainer using pose estimation. Using the pose estimation model running on the CPU, we will identify specific points on a person's body to calculate angles and gestures. This allows us to determine the number of bicep curls a person has performed. We will write the code in a way that allows us to easily find angles between any three points with just a single line of code. Additionally, we will explore how to Create real computer vision apps with object detection, augmented reality, document scanning, and more.

Introduction

Welcome to this tutorial on creating a personal AI trainer using pose estimation. In this video, we will learn how to utilize the pose estimation model to detect specific points on a person's body, calculate angles between these points, and use these calculations to count the number of bicep curls performed. This technology can also be applied to other activities such as yoga postures or correct form in various exercises. We will begin by setting up our project in PyCharm and importing the necessary packages.

Setting up the Project in PyCharm

To start our project, we will set up our development environment in PyCharm. We will import the required packages such as OpenCV, NumPy, and time. These packages are crucial for image processing, data manipulation, and time calculations, respectively. Once our project is set up, we can move on to the actual implementation.

Using the Pose Estimation Module

Our project will rely on a pose estimation module to detect specific landmarks on a person's body. We will import and utilize this module to find the pose in images and videos. By using the pose detection functionality, we will retrieve a list of landmarks for each detected person. These landmarks will serve as reference points for calculating angles and detecting gestures.

Creating the AI Trainer

Next, we will create our AI trainer module. This module will contain methods to find angles between three specified landmarks, as well as methods to detect bicep curls through angle calculations. We will develop a systematic approach that allows for flexible input of landmark numbers, making our code reusable and adaptable to different scenarios. By specifying the landmark numbers corresponding to the left or right arm, we can accurately count the number of curls performed by an individual.

Finding Angles Between Landmarks

Now that we have set up our AI trainer module, we can focus on the Core functionality of our trainer: finding angles between landmarks. We will implement a method that takes three landmark points as input and calculates the angle using trigonometry. By using these calculated angles, we can determine the extent of bicep curls and track the progress of each curl.

Counting Curls with Gesture Detection

To count the number of bicep curls performed, we will implement a counting mechanism using gesture detection. By checking the position and movement of the calculated angles, we can determine when a curl is completed. We will keep track of the curl count and update it accordingly each time a curl gesture is detected. This mechanism allows us to easily track and monitor the progress of an individual's workout routine.

Conclusion

In conclusion, creating a personal AI trainer using pose estimation can greatly enhance workout tracking and form correction. By utilizing the pose estimation model and implementing angle calculations, gesture detection, and curl counting mechanisms, we can accurately monitor and track the progress of bicep curls. Additionally, this technology can be further developed to extend its applications to other workout routines and activities. Remember to experiment and tailor the code to suit your specific needs and enjoy the benefits of having a personal AI trainer to assist you in achieving your fitness goals.

Highlights

• Utilize pose estimation to detect landmarks on a person's body
• Calculate angles between specific points to track bicep curls
• Create an AI trainer module for flexible and reusable code
• Use gesture detection to count the number of curls performed
• Monitor and track progress with a personal AI trainer

FAQ

Q: Can I use this AI trainer for other workout routines? A: Yes, you can modify the code to track angles and gestures for different exercises such as leg curls, yoga postures, or any other exercise that can be monitored based on landmarks.

Q: How accurate is the pose estimation model? A: The accuracy of the pose estimation model depends on various factors such as lighting conditions, camera angle, and the person's clothing. It is recommended to experiment with different scenarios and perform regular validations to ensure accurate results.

Q: Can I use a webcam instead of a video file? A: Yes, you can modify the code to use a webcam by providing the appropriate webcam ID as input. This allows you to receive real-time feedback and track your workout performance on the go.

Q: Can I customize the code further to suit my specific requirements? A: Absolutely! The code provided serves as a foundation that can be adapted and expanded upon. Feel free to add additional features, modify the code structure, or integrate it into your existing projects to create a personalized AI trainer that meets your exact needs.