ASLingo
Camera Integration: Utilize Apple's AVFoundation framework to access the iPhone's camera and display a live feed within your app. You'll need to set up a AVCaptureSession to capture video from the camera and display it on the screen using a AVCaptureVideoPreviewLayer.
Display ASL Letter Images: When users start a lesson, display images of ASL letters one at a time on top of the live camera feed. You can use UIImageView to display these images on the screen.
User Gesture Recognition: Implement gesture recognition to detect the user's hand gestures as they mimic the displayed ASL letter. You can use techniques like hand tracking or contour detection to recognize hand shapes and movements. Core ML may also be used for this purpose, depending on your chosen approach.
Comparison and Feedback: Compare the user's gestures with the correct ASL letter and provide feedback on whether they are correct or incorrect. You can use image processing techniques to compare hand shapes and movements.
Progress Tracking: Keep track of the user's progress throughout the lesson. Record which ASL letters they successfully mimic and which ones they struggle with.
Lesson Review: After completing the lesson, present users with a review section where they can practice mimicking ASL letters without the aid of images. Prompt them with the name of the letter they need to sign and provide feedback based on their gestures.
Reinforcement: As users progress through the lesson, reinforce their learning by gradually reducing the amount of visual aid provided. For example, initially display both the ASL letter image and its name, then later only display the name, and finally prompt users to sign the letter without any visual aid.
Build and Train the Model: Convolutional neural networks (CNNs) TensorFlow or Keras.
Convert Model to Core ML: Once model is trained convert it to Core ML format for deployment on iOS devices. TensorFlow provides tools for converting trained models to Core ML format. You can use the TensorFlow Lite Converter to convert your TensorFlow model to TensorFlow Lite format, and then use Apple's Core ML Tools to convert it to Core ML format. If you're using Keras, you can use the coremltools library to convert your Keras model directly to Core ML format. Integrate Core ML Model into iOS App:
Add the Core ML model file (.mlmodel) to your Xcode project. Use Core ML's APIs in Swift to load the model and make predictions on input images in real-time within your iOS app.
Choose or Train a Hand Gesture Recognition Model:
You can either train your own model for hand gesture recognition using techniques like deep learning on labeled hand gesture datasets, or you can use pre-trained models available for this purpose. There are several deep learning architectures suitable for this task, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs). Convert the Model to Core ML:
Once you have a trained hand gesture recognition model, convert it to the Core ML format using Apple's Core ML Tools. This will allow you to integrate it into your iOS app. Follow the same steps mentioned earlier for converting your TensorFlow model to Core ML format. Integrate Core ML Model into iOS App:
Add the Core ML model file (.mlmodel) to your Xcode project. Use Core ML's APIs in Swift to load the model and perform inference on live video frames captured from the device's camera. Process the video frames to extract hand regions or keypoints, and feed them into the Core ML model for gesture recognition. You may need to preprocess the input frames and post-process the model predictions to extract meaningful gesture information. Implement Real-Time Gesture Recognition:
Use AVFoundation framework to access the device's camera and capture live video frames. Process each frame using computer vision techniques (e.g., background subtraction, skin detection, hand segmentation) to isolate hand regions or keypoints. Feed the processed frames into the Core ML model for gesture recognition. Based on the model's predictions, determine the detected hand gesture and update the app's interface accordingly (e.g., display the recognized ASL letter).