https://github.com/marat200118/creative-code-3-learning-machine-assignment
Devine - Creative Code 3 (The squat Alarm made with PoseNet and KNN clarifier, to switch the alarm off, make some squats)
https://github.com/marat200118/creative-code-3-learning-machine-assignment
knn-classification machine-learning ml5js pose-net pose-recognition
Last synced: about 1 year ago
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Devine - Creative Code 3 (The squat Alarm made with PoseNet and KNN clarifier, to switch the alarm off, make some squats)
- Host: GitHub
- URL: https://github.com/marat200118/creative-code-3-learning-machine-assignment
- Owner: Marat200118
- Created: 2023-09-28T08:29:12.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-26T11:16:36.000Z (over 2 years ago)
- Last Synced: 2025-01-28T01:38:34.258Z (over 1 year ago)
- Topics: knn-classification, machine-learning, ml5js, pose-net, pose-recognition
- Language: JavaScript
- Homepage: https://marats-samigullins.com/GetUpYouLazy/
- Size: 4.71 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Pose Exercise Alarm 🏋️♂️⏰
Devine - Creative code 3 course
A unique alarm application that utilizes machine learning models to track user movements and turn off the alarm only when the user completes a set number of exercises!
# Overview
The Pose Exercise Alarm system combines the power of the KNN classifier with the PoseNet model to detect user-defined movements. Users can set an alarm and the only way to turn it off is by doing the specified number of squats.
# Features
1. Live Video Feed: Capture and display the user's pose in real-time.
2. Customizable Alarm Settings: Set the alarm time, choose an exercise, and define the number of repetitions.
3. Pose Detection: Utilizes the PoseNet model for accurate pose detection.
4. Interactive Interface: Visual feedback on the number of repetitions and the confidence level of the pose detection.
5. Model Training & Saving: Ability to add data for poses and save/load the KNN model.
# Usage
1. Set the Alarm: Select the desired alarm time.
2. Choose an Exercise: For now, we support squats but more exercises can be added in the future.
3. Define Repetitions: Set the number of repetitions required to turn off the alarm.
4. Train the Model:
-- Click the "Class A" button while performing the exercise (e.g., in a squat position).
-- Click the "Class B" button while standing up.
-- Add enough samples for both classes for accurate predictions.
5. Save/Load Model: After training, you can save the model for future use and load it whenever needed.
When the alarm time arrives, perform the specified number of repetitions to stop the alarm.