/ai-workout-assistant

AI-based pose tracking and repetitions counter to help everyone do the workout.

Primary LanguageJavaScriptMIT LicenseMIT

example workflow

AI Workout Assistant

An web application to help everyone do workout wherever and whenever. Supported by the pose detector feature to analyze every pose and auto count the number of repetitions made. So, let's create a healthy life by do workout every day!

AI Workout Assistant Thumbnail

Pose Detector and Classification in the AI Workout Assistant application fully runs on the Client side, so no image data comes out of the user's device. Videos or images processed by the program will be automatically deleted.

How it Work

Image data which is obtained from video or webcam will be processed by pose detector using the MoveNet model to generate keypoints. Keypoints are used for repetition calculations and input for classifying workout types with Dense Neural Network (DNN) model.

How AI Workout Work

How to Run Locally

  • Prerequisites: you'll need to have Git, Node, and NPM installed and running on your machine.
  • Open terminal/powershell/command prompt then clone this repository 
    git clone https://github.com/reevald/ai-workout-assistant.git
cd ai-workout-assistant
  • Install dependencies 
    npm install
  • Once the installation is done, you can run the app locally 
    npm run start-dev
  • Then open http://localhost:8080 to see your app.

Generate Your Own Workout

  1. Open app locally or visit aiworkout.live
  2. Collect dataset (keypoints):
    • Using webcam (open settings menu => turn on Developer Mode => click Record Keypoints button then click again when finish recording)
      Note: (1) The app only records keypoints while its playing and the result will be csv format; (2) The value of each keypoint always in resolution 640x360 (when use webcam only).
    • Via upload video (open settings menu => turn on Developer Mode => click Upload Video)
      Note: (1) The value of each keypoint will be dependence with resolution of video; (2) High resolution of video will be reduce fps; (3) You can switch video to webcam (or vice-versa) while recording (not recommended can cause different resolution of keypoints).
  3. Generate new model and train as well
    The model is binary classification to determine workout or not. Example: push-up (positive) or not push-up (negative). In the step 2 above, we have been collect positive class. For negative class, you can use this video as negative class data (convert first to get keypoints with step 2 above). Then after collect positive and negative data, open this colab and follow the instruction. You will be get the model with tfjs format which will be use on the next step.
    Note: to continue next step you need to run app locally (check How to Run above)
  4. Put the model into the app
    First create new folder ./public/tfjs-model/your-workout then move tfjs model and bin file into the new folder.
  5. Create new configuration

    • Change file: ./public/mock-data/workout.json

      "nameWorkout": ["Push Up", "Squat", "Your Workout"],
"slugWorkout": ["push-up", "squat", "your-workout"],
"duration": ["1 Minutes", "3 Minutes", "5 Minutes", "7 Minutes"]

      Note:

      • nameWorkout: replace Your Workout with your workout name.
      • For slugWorkout part your-workout must match with model folder name.
      • You can change the duration in minutes.

    • Create new file: ./public/rules/your-workout.json with config like this
      Important: Please using ./public/rules/push-up.json as reference

      {
    "poseDetectorConfig": {
        "model": "MoveNet",
        "detectorConfig": {},
        "estimationConfig": {}
    }
}

      Note :

      {
    "classifierConfig": {
        "path": "./tfjs-model/your-workout/model.json",
        "label": ["Other", "Your Workout"]
    }
}

      Note:

      • path: change with your model path
      • label: must ordered by alphabet
      {
    "rulesCountConfig": {
        "nameWorkout": "Your Workout",
        "nameStage": ["Down", "Up"],
        "pathImageStage": ["./img/iconmonstr-caret-down-circle-filled-64.png", "./img/iconmonstr-caret-up-circle-filled-64.png"],
        "pathAudioStage": ["./audio/go-down-from-google-translate.webm", "./audio/go-up-from-google-translate.webm"],
        "anglePoint": {
            "7": {
                "spouseIdx": [9, 5],
                "rangeAngle": [
                    { "min": 0, "max": 90 },
                    { "min": 150, "max": 180 }
                ]
            }
        }
    }
}

      Note

      • nameWorkout: replace Your Workout with your workout name.
      • nameStage: for example in push-up there are two stages Down and Up. The order is important, if start with Down, then 1 repetition equal to Down->Up.
      • pathImageStage: path-image (put first into ./public/img folder) or url-image (outside source)
      • pathAudioStage: prefer using webm format and it has short duration < 2 seconds. path-audio (put first into ./public/audio folder) or url-audio (outside source).
      • anglePoint: for example index keypoint 7 has spouse index keypoint 9 and 5. rangeAngle is range to each stage, ex: Down stage has range angle between 0 and 90 degree. To help determine index keypoints, please look at image below: Ilustration Keypoints
        Source image: (https://github.com/tensorflow/tfjs-models/tree/master/pose-detection)
  6. Run the app to testing 
    npm run start-dev
    Then open http://localhost:8080 to see your own workout and have fun!

Limitations

  • Cannot cover 3D angle yet
    • Planning: research lightweight model that can generate 3D keypoints like BlazePose, MoVNect, LHPE-nets or other model.
  • Currently the high resolution makes fps slower
    • You can try upload videos with high and low resolution then compare them and watch the fps.
    • Solution to webcam used: using limit with fixed "real" resolution 640x360. To display variate resolution screen, in this case we are using css manipulation.

References

TODOs

  • Write documentations
  • Write unit test
  • [Done] Add audio effect (timer and movement direction)
  • Data Augmentation (flip horizontal, scale, shear and shift) to try improve metric pose classification model (accuration)
  • Convert to components (prefer using framework like react js)