Technical Details

AI-Powered Movement Detection

The Stretch Reminder App leverages cutting-edge Computer Vision and Machine Learning technologies to provide accurate stretching detection. This combination allows the app to effectively distinguish between genuine stretching movements and regular activity.

Motion Detection System

The Stretch Reminder App uses a sophisticated combination of OpenCV for basic motion detection and TensorFlow.NET for advanced pose detection capabilities.

Stretch Detection

OpenCV Implementation Details

Computer Vision Technology

The app leverages computer vision techniques through OpenCvSharp4 to process and analyze webcam video frames in real-time, detecting user movements with precision.

The current motion detection system implements a multi-stage approach using OpenCvSharp4, a powerful .NET wrapper for OpenCV:

  1. Frame Acquisition: Capture video frames from the webcam using VideoCapture
  2. Pre-processing:
    • Convert frames to grayscale using Cv2.CvtColor with ColorConversionCodes.BGR2GRAY
    • Apply Gaussian blur with Cv2.GaussianBlur using a 21x21 kernel to reduce noise
  3. Motion Detection:
    • Store previous frame for comparison
    • Calculate absolute difference between frames using Cv2.Absdiff
    • Apply binary threshold with Cv2.Threshold to create a binary image where white pixels represent motion
  4. Noise Reduction:
    • Apply morphological operations:
      • Cv2.Erode to remove small noise points
      • Cv2.Dilate to enhance true motion areas
  5. Movement Quantification:
    • Sum all white pixels with Cv2.Sum to get a numerical value for motion intensity
    • Compare against dynamic threshold to identify significant movement
  6. Sequential Validation:
    • Track consecutive frames with motion to filter out random spikes
    • Only trigger “stretch detected” when multiple consecutive frames show significant motion
// Core motion detection implementation
private async Task<bool> ProcessFrameForMotionAsync(Mat frame)
{
    // Convert to grayscale for processing
    using (Mat grayFrame = new Mat())
    {
        Cv2.CvtColor(frame, grayFrame, ColorConversionCodes.BGR2GRAY);
        Cv2.GaussianBlur(grayFrame, grayFrame, new Size(21, 21), 0);
        
        // Compute difference between current and previous frame
        using (Mat diff = new Mat())
        {
            Cv2.Absdiff(grayFrame, _previousFrame, diff);
            
            // Apply threshold to ignore small movements
            Cv2.Threshold(diff, diff, 30, 255, ThresholdTypes.Binary);
            
            // Apply morphological operations to filter noise
            var kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(5, 5));
            Cv2.Erode(diff, diff, kernel, iterations: 1);
            Cv2.Dilate(diff, diff, kernel, iterations: 2);
            
            // Compute amount of motion (sum of all white pixels)
            double motionAmount = Cv2.Sum(diff)[0];
            
            // Track consecutive motion frames for robustness
            bool currentFrameHasMotion = motionAmount > _motionThreshold && !_isCalibrating;
            if (currentFrameHasMotion)
                _consecutiveMotionFrames++;
            else
                _consecutiveMotionFrames = 0;
                
            // Only consider it stretching if we have enough consecutive motion frames
            bool isStretching = _consecutiveMotionFrames >= _requiredConsecutiveFrames;
            
            return isStretching;
        }
    }
}

Key OpenCV Features Utilized

The motion detection system leverages several fundamental OpenCV capabilities:

  • Image Processing Operations: For noise reduction and feature extraction
  • Binary Thresholding: To distinguish between significant and insignificant motion
  • Morphological Transformations: To clean up the binary image and enhance detection quality
  • Frame Differencing: To identify changes between consecutive frames
  • Visual Feedback: Overlaying detection status and metrics on the video feed

TensorFlow.NET Integration

Machine Learning Technology

The application incorporates TensorFlow.NET to deploy advanced neural networks that can recognize human poses and specific stretching movements with high accuracy.

The app integrates TensorFlow.NET to enhance the motion detection with machine learning-based pose estimation. This integration enables:

  1. Human Pose Detection: Identifying key body joints and their positions
    • Tracks 17 key body points including shoulders, elbows, wrists, hips, knees, and ankles
    • Calculates joint angles to verify proper stretching form
    • Operates at 15-30 FPS on standard hardware
  2. Pose Classification: Recognizing specific stretching poses
    • Distinguishes between different types of stretches (arm, neck, back, etc.)
    • Validates proper execution against reference models
    • Provides real-time feedback on stretch quality
  3. Movement Quality Analysis: Analyzing the effectiveness of stretches
    • Measures range of motion during stretches
    • Detects incomplete or improper stretching movements
    • Offers guidance for improving stretch effectiveness
// TensorFlow model loading (pseudocode - implementation in progress)
private void LoadPoseDetectionModel()
{
    // Load TensorFlow model for pose detection
    using var graph = new TF.Graph();
    using var session = new TF.Session(graph);
    
    // Load saved model from disk
    graph.Import(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "Models", "pose_detection_model"));
    
    _poseDetectionModel = session;
}

// Pose detection implementation (pseudocode - implementation in progress)
private async Task<PoseData> DetectPoseAsync(Mat frame)
{
    // Convert frame to tensor format
    var tensor = ImageToTensor(frame);
    
    // Run inference
    var output = _poseDetectionModel.Run(
        new[] { _poseDetectionModel.Graph["input"][0] },
        new[] { tensor },
        new[] { _poseDetectionModel.Graph["output"][0] }
    );
    
    // Process detected keypoints
    return ProcessPoseOutput(output);
}

Automatic Calibration System

The calibration system uses statistical analysis of motion patterns to establish baseline thresholds, ensuring reliable detection across different environments and hardware configurations.

A key aspect of the movement detection is the automatic calibration system, which:

  1. Baseline Establishment: Captures baseline motion data during the first few seconds 
    _backgroundMotion = (_backgroundMotion * (_calibrationFrames - 1) + motionAmount) / _calibrationFrames;
  2. Adaptive Thresholds: Establishes dynamic thresholds based on the user’s environment 
    _motionThreshold = Math.Max(150000, _backgroundMotion * 10); // Set threshold higher than baseline
  3. Environment Adaptation: Adjusts sensitivity parameters based on detected ambient conditions 
    // Dynamically adjust parameters based on calibration results
_detectionCooldown = _backgroundMotion > 100000 
    ? TimeSpan.FromSeconds(2.5)
    : TimeSpan.FromSeconds(1.5);

This calibration ensures the app works well in various lighting conditions and with different webcam qualities.

Future Enhancements

AI Roadmap

Our development roadmap prioritizes enhanced machine learning capabilities to provide a more personalized and effective stretching experience through advanced AI techniques.

The TensorFlow integration is currently being refined to improve:

  1. Specific Pose Recognition: Better identification of different stretch types
    • Training on a broader dataset of stretching movements
    • Implementing transfer learning from larger pre-trained models
    • Supporting a library of 20+ specific stretching exercises
  2. Posture Analysis: Feedback on stretch quality and form
    • Real-time correction suggestions for improper form
    • Tracking improvement in stretching technique over time
    • Personalized difficulty adjustment based on flexibility
  3. Personalized Recommendations: Custom stretch suggestions based on user history
    • Analyzing stretching patterns to identify areas needing focus
    • Recommending targeted exercises for specific muscle groups
    • Adapting to user preferences and physical capabilities

These enhancements will make the app more accurate in detecting genuine stretching movements and reduce false positives.

Copyright © 2025 Stretch Tracker App. Distributed under the MIT license.