Build AI Fitness Apps in Flutter with Pose Detection

Install the Flutter SDK on Mac, create a development folder, add Flutter to your path, and verify with flutter version to set up Flutter app development.

Set up Android Studio for Flutter development by downloading from developer.android.com, installing the SDK components, and adding the Flutter plugin, then restart to create Flutter projects.

Learn to install and set up Xcode on macOS, then combine Android Studio with Xcode to build Flutter apps for iOS and macOS.

Create a new flutter project in Android Studio, select Kotlin for Android and Swift for iOS, and install the default counter app on both iOS simulator and Android emulator.

Learn to set up and launch an Android emulator, selecting a Pixel 7 device with Android 14, downloading the system image, and installing your Flutter app inside the emulator.

Install flutter on Windows by downloading the flutter sdk from flutter.dev, extracting it, placing it on the C drive, and configuring environment variables for flutter bin, then run flutter doctor.

Install Android Studio, configure Flutter by installing the Dart and Flutter plugins, restart the IDE, and accept Android SDK licenses to enable Flutter development.

Create and launch an Android virtual device in Android Studio using Virtual Device Manager to test apps on Android 14, selecting devices like Pixel 7 Pro and configuring orientation.

Create a flutter image picker app by building a graphical user interface with an elevated button to choose or capture images from gallery or camera, then display the selected image.

Add the image picker library from pub.dev to a Flutter project, install via pubspec.yaml and pub get, and configure iOS permissions to access gallery and camera; Android needs no setup.

Learn to implement Flutter's image picker to select from gallery, initialize in init state, handle async pick image, convert to a file, and display it with setState.

Learn to capture images in a Flutter app by long-pressing a button to launch the camera via image_picker, display the captured image, and test on an Android emulator.

Learn to build a Flutter image picker app that lets users choose images from the gallery or capture with the camera. Review library setup, permissions, and displaying the selected image.

Clone and run the starter Flutter project for pose estimation, explore the GUI, and learn to load, capture, and display images with image picker.

Add Google ML Kit pose detection library via pub.dev, then configure Android sdk requirements (min 21, target 33, compile 34) in build.gradle to enable pose detection.

Convert the selected image to input image format and pass it to the pose detector to obtain a single pose with landmarks, since the model detects one person per image.

Draws pose points and lines on an image in Flutter using a custom painter called pose painter; converts the image format, passes poses, and renders on a canvas.

Draw red circles at body joint locations on the image by iterating poses and landmarks with a canvas draw circle method, using a paint object to set color and stroke.

Improve pose detector accuracy in Flutter by switching to the accurate model and single-image mode, test with various images, and draw lines joining these points.

Draw a digital skeleton in Flutter by connecting pose landmarks with lines, using a reusable draw custom line method to render arms, shoulders, hips, and legs.

Enhance the app with yoga pose detection that analyzes body joints, draws lines between joints, and provides feedback on poses like downward dog.

In Flutter, build a yoga pose detection feature that analyzes body landmarks, validates downward dog and upward dog poses, calculates angles, and displays real-time feedback in the UI.

Learn how flutter detects the downward dog pose using body landmarks, angle checks, and visibility validation, guiding users to straighten arms or lift the hip to form inverted v.

Enhance a Flutter yoga detector by using ChatGPT to write an upward dog pose method, leveraging pose landmarks and a post-detection model to provide real-time feedback on captured images.

Create a real-time flutter app by starting a project and adding the camera library to display live footage and pass frames to ML models with iOS and Android permissions.

Display live camera footage in Flutter using the camera package, initialize a CameraController with available cameras, pass it to CameraPreview, handle initialization and permissions, and replace the counter UI.

Capture live camera frames one by one in flutter with a controller and start image stream, feed each frame to a real-time ML model, and display results.

Display live camera footage in a Flutter app with the camera package, pubspec.yaml, iOS and Android permissions, listing cameras, and streaming frames via a camera controller and start image stream.

Clone the starter Flutter app from GitHub, install it in an Android emulator or iOS simulator, grant camera permissions, and explore the stack overlay for real-time pose detection.

Pass live camera frames to a Flutter app's pose detector using Google ML Kit in stream mode, with a guard to draw body joints in real time for exercise counting.

Learn to display real-time body joints in Flutter by overlaying lines and circles on a live camera feed using a custom painter and a transparent overlay widget.

Learn to render a real time pose skeleton in Flutter by extending a custom painter, drawing green circles at body landmarks on a canvas and connecting joints with lines.

Import the starter app code and display live camera footage with a camera package, then run pose detection on frames. Render landmarks and connections in real time on a canvas.

Build a real-time exercise detector and counter in Flutter using pose detection, counting pushups, squats, plank to downward dog, and jumping jacks as users tap each exercise tile.

Add a bottom-centered, rounded black container with a text widget in white to display the exercise rep count, overlaid on the Flutter UI using a stack.

Learn to detect pushups in real time and count repetitions in a Flutter app using pose detection, with sample code and integration guidance.

Learn to detect pushups, squats, planks, and other exercises in Flutter using pose landmarks and elbow angle calculations, then count reps through a responsive GUI.

Create an exercise listing screen in Flutter by building a stateful widget inside a scaffold, and display four deep purple, rounded tiles in a list with exercise names and gifs.

Copy the assets folder into your Flutter project, declare it in pubspec.yaml, and display GIFs with image.asset in a dynamic list view, aligning center or center right for four exercises.

Define an exercise data model with title, image, color, and type, load data to populate a dynamic list view, then tap an item to open a detection screen.

Create a detection screen to run live camera footage, detect and count exercises, and navigate from the list view by tapping a tile using Inkwell and MaterialPageRoute.

Pass exercise data model objects to the detection screen and dispatch detection by exercise type to count push ups, squats, plank to downward dog, and jumping jacks.

Enhance the exercise detection screen with live camera footage, a color-coded count circle, and a title box bound to the exercise data model's color and title, plus an optional gif.

Learn to replace an integer exercise type with an enum in a Flutter data model, clarifying codes for pushups, squats, downward dog, and jumping jack, and enabling detection and extension.

Enhance the Flutter pose detection app by adding high knees detection, updating the exercise list, and using ChatGPT to generate the detection method, then verify accurate counting.

Replace the in-app exercise media by downloading or creating a matching GIF, add it to assets, and customize pose-detection methods for specific exercises or yoga poses using ChatGPT.