Stretch Tracker: Advanced Technical Architecture and Implementation

Stretch Tracker : Advanced Technical Architecture and Implementation

The Birth of the Stretch Tracker: A Developer’s Story

As developers, we often find ourselves deeply immersed in our work, sitting for hours on end in front of our screens without even realizing how much time has passed. I was no different. During a particularly intensive project, I noticed the toll these long coding sessions were taking on my physical health - back pain, stiff neck, and decreased productivity became unwelcome companions in my daily routine.

One evening, while taking a rare break and browsing through a fitness app, I had a realization: most productivity apps remind you to take breaks, but they don’t actually ensure you’re using that break effectively for your health. That’s when the idea struck me - what if I built an application that not only reminds developers to stretch periodically but also uses computer vision to verify they’re actually performing their stretches?

The Stretch Tracker was born from this personal need - an intelligent health companion that would:

1. Send timely, customizable reminders to take stretch breaks
2. Use the computer’s camera to detect when stretching exercises are being performed
3. Employ machine learning and pose estimation to validate proper stretching technique
4. Track stretching consistency and progress over time, providing encouragement to maintain healthy habits

This wasn’t just about creating another notification tool; it was about developing technology that bridges the gap between knowing what’s good for us and actually doing it. As developers, we build solutions to problems - and this was a solution to a problem we all face.

System Architecture Overview

The Stretch Tracker is a sophisticated, multi-layered application that combines multiple technologies to create an intelligent health monitoring system.

graph TD
    A[User Interaction Layer] --> B[Presentation Layer - WPF]
    B --> C[Core Logic Layer]
    C --> D[Computer Vision Module - OpenCvSharp4]
    C --> E[Machine Learning Module - TensorFlow.NET]
    C --> F[Persistence Layer - SQLite]
    
    D --> G[Frame Processing]
    E --> H[Pose Estimation]
    F --> I[Data Tracking]
    
    G --> J[Motion Detection Algorithm]
    H --> K[Stretch Validation]
    I --> L[User Statistics]

Technical Component Breakdown

1. Application Framework: .NET 9.0 WPF

Architectural Principles

• MVVM (Model-View-ViewModel) Pattern
• Dependency Injection
• Asynchronous Programming

Core Project Configuration

<Project Sdk="Microsoft.NET.Sdk">
  <PropertyGroup>
    <OutputType>WinExe</OutputType>
    <TargetFramework>net9.0-windows</TargetFramework>
    <UseWPF>true</UseWPF>
  </PropertyGroup>

  <ItemGroup>
    <PackageReference Include="Hardcodet.NotifyIcon.Wpf" Version="2.0.1" />
    <PackageReference Include="OpenCvSharp4" Version="4.10.0.20241108" />
    <PackageReference Include="TensorFlow.NET" Version="0.150.0" />
    <PackageReference Include="Microsoft.Data.Sqlite" Version="9.0.0" />
  </ItemGroup>
</Project>

2. Computer Vision Module: Advanced Motion Detection

Core Detection Algorithm

private async Task<bool> ProcessFrameForMotionAsync(Mat frame)
{
    // Preprocessing pipeline
    using (Mat grayFrame = new Mat())
    {
        // Convert to grayscale for consistent processing
        Cv2.CvtColor(frame, grayFrame, ColorConversionCodes.BGR2GRAY);
        
        // Noise reduction using Gaussian blur
        Cv2.GaussianBlur(grayFrame, grayFrame, new Size(21, 21), 0);

        // Frame difference computation
        using (Mat diff = new Mat())
        {
            Cv2.Absdiff(grayFrame, _previousFrame, diff);
            
            // Binary thresholding to isolate significant movements
            Cv2.Threshold(diff, diff, 30, 255, ThresholdTypes.Binary);
            
            // Morphological operations for noise filtering
            var kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(5, 5));
            Cv2.Erode(diff, diff, kernel, iterations: 1);
            Cv2.Dilate(diff, diff, kernel, iterations: 2);

            // Motion quantification
            double motionAmount = Cv2.Sum(diff)[0];
            
            // Adaptive thresholding
            bool isSignificantMotion = motionAmount > _dynamicMotionThreshold;
            
            return isSignificantMotion;
        }
    }
}

Advanced Detection Techniques

1. Adaptive Thresholding
   • Dynamically adjusts detection sensitivity
   • Learns background noise levels
   • Minimizes false positives
2. Multi-Stage Motion Analysis
   • Grayscale conversion for consistent processing
   • Gaussian blur for noise reduction
   • Frame differencing to isolate changes
   • Morphological operations for refined detection

3. Machine Learning Integration: TensorFlow Pose Estimation

public class PoseDetectionModel
{
    private TF.Session _poseDetectionSession;
    private const int KeypointCount = 17;

    public async Task<PoseDetectionResult> DetectPose(Mat frame)
    {
        // Convert OpenCV Mat to TensorFlow tensor
        var inputTensor = ConvertMatToTensor(frame);
        
        // Run inference through neural network
        var outputs = _poseDetectionSession.Run(
            new[] { _graph.GetTensorByName("input_tensor") },
            new[] { inputTensor },
            new[] { 
                _graph.GetTensorByName("keypoints_output"),
                _graph.GetTensorByName("confidence_output")
            }
        );

        // Process detected keypoints
        var keypointsRaw = outputs[0];
        var confidenceScores = outputs[1];

        return ProcessPoseKeypoints(keypointsRaw, confidenceScores);
    }

    private PoseDetectionResult ProcessPoseKeypoints(Tensor keypointsTensor, Tensor confidenceTensor)
    {
        var keypoints = new List<Keypoint>();
        
        for (int i = 0; i < KeypointCount; i++)
        {
            var x = keypointsTensor[0, i, 0];
            var y = keypointsTensor[0, i, 1];
            var confidence = confidenceTensor[0, i];

            keypoints.Add(new Keypoint(
                x, y, 
                (KeypointType)i, 
                confidence
            ));
        }

        return new PoseDetectionResult(keypoints);
    }
}

// Supporting data structures
public enum KeypointType 
{
    Nose, Neck, RightShoulder, RightElbow, // ... and so on
}

public record Keypoint(
    float X, 
    float Y, 
    KeypointType Type, 
    float Confidence
);

public record PoseDetectionResult(List<Keypoint> Keypoints)
{
    public bool IsValidStretch() 
    {
        // Complex validation logic
        // Analyze keypoint relationships
        // Determine if current pose represents a stretch
    }
}

4. Persistence Layer: SQLite Database Management

public class DatabaseManager
{
    private readonly string _connectionString;

    public void InitializeDatabase()
    {
        using var connection = new SqliteConnection(_connectionString);
        connection.Open();

        // Create stretching sessions table
        using var command = connection.CreateCommand();
        command.CommandText = @"
            CREATE TABLE IF NOT EXISTS StretchSessions (
                Id INTEGER PRIMARY KEY AUTOINCREMENT,
                Date TEXT NOT NULL,
                Completed INTEGER NOT NULL,
                Duration INTEGER NOT NULL
            )";
        command.ExecuteNonQuery();
    }

    public void RecordStretchSession(bool completed, int durationSeconds)
    {
        using var connection = new SqliteConnection(_connectionString);
        connection.Open();

        using var command = connection.CreateCommand();
        command.CommandText = @"
            INSERT INTO StretchSessions (Date, Completed, Duration)
            VALUES (@date, @completed, @duration)";

        command.Parameters.AddWithValue("@date", DateTime.Now.ToString("yyyy-MM-dd"));
        command.Parameters.AddWithValue("@completed", completed ? 1 : 0);
        command.Parameters.AddWithValue("@duration", durationSeconds);

        command.ExecuteNonQuery();
    }

    public int GetCurrentStreak()
    {
        int streak = 0;
        DateTime currentDate = DateTime.Now.Date;

        using var connection = new SqliteConnection(_connectionString);
        connection.Open();

        // Complex streak calculation logic
        // Checks consecutive days of completed stretches
        // ...

        return streak;
    }
}

5. Advanced Configuration Management

public class AppSettings
{
    // Configurable parameters
    public int NotificationIntervalMinutes { get; set; } = 120;
    public int RequiredStretchCount { get; set; } = 5;
    public float PoseDetectionThreshold { get; set; } = 0.7f;

    public void Save()
    {
        // JSON serialization of settings
        var json = JsonSerializer.Serialize(this, 
            new JsonSerializerOptions { WriteIndented = true });
        File.WriteAllText(_settingsPath, json);
    }

    public static AppSettings Load()
    {
        // Robust settings loading with fallback
        try 
        {
            var json = File.ReadAllText(_settingsPath);
            return JsonSerializer.Deserialize<AppSettings>(json) 
                   ?? new AppSettings();
        }
        catch 
        {
            return new AppSettings(); // Default configuration
        }
    }
}

Critical Focus Areas for Improvement

1. Motion Detection Refinement

• Adaptive Thresholding: Continuously learn and adjust detection sensitivity
• Noise Reduction: Improve morphological operation techniques
• Multi-frame Analysis: Enhance consecutive frame validation

2. Pose Estimation Enhancement

• Keypoint Accuracy: Improve neural network model precision
• Stretch Classification: Develop more sophisticated stretch type recognition
• Movement Quality Assessment: Create detailed stretch quality metrics

3. Machine Learning Model Considerations

• Model Selection:
  • Lightweight models for real-time performance
  • High accuracy in pose estimation
  • Low computational overhead
• Potential Models to Explore:
  1. MoveNet
  2. PoseNet
  3. BlazePose
  4. OpenPose Lite

4. Performance Optimization Strategies

• Asynchronous Processing
• Tensor Preprocessing Efficiency
• Minimal Memory Allocation
• GPU Acceleration Support

Implementation Challenges

1. Variability in Human Movement

• Different body types
• Varying stretching techniques
• Environmental variations

2. Real-time Processing Constraints

• Maintain 30+ FPS
• Minimal computational resource usage
• Consistent detection accuracy

Future Technical Roadmap

1. Enhanced Pose Classification
   • More granular stretch type detection
   • Machine learning model retraining
2. Cross-Platform Support
   • .NET MAUI for multi-platform deployment
   • Unified codebase
3. Advanced Analytics
   • Predictive health insights
   • Machine learning-driven recommendations

Conclusion: Technology Meets Wellness

The Stretch Reminder App demonstrates how modern software development can create intelligent, health-focused solutions by combining:

• Computer vision
• Machine learning
• Robust application architecture
• User-centric design

By leveraging cutting-edge technologies, we transform a simple reminder into an intelligent health companion.

Get Involved!

• Try the Code: Test the examples provided and share your results in the comments. May be Flutter version of this app.

• Follow Us: Stay updated on new developments by following the project on. GitHub.