Building ErgoVision: A Developer&#s Journey in AI Safety

Introduction

Hey dev community! ? I'm excited to share the journey of building ErgoVision, an AI-powered system that's making workplaces safer through real-time posture analysis. Let's dive into the technical challenges and solutions!

The Challenge

When SIIR-Lab at Texas A&M University approached me about building a real-time posture analysis system, we faced several key challenges:

1. Real-time processing requirements
2. Accurate pose estimation
3. Professional safety standards
4. Scalable implementation

Technical Stack

# Core dependencies
import mediapipe as mp
import cv2
import numpy as np

Why This Stack?

• MediaPipe: Robust pose detection
• OpenCV: Efficient video processing
• NumPy: Fast mathematical computations

Key Implementation Challenges

1. Real-time Processing

The biggest challenge was achieving real-time analysis. Here's how we solved it:

def process_frame(self, frame):
    # Convert to RGB for MediaPipe
    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    results = self.pose.process(rgb_frame)

    if results.pose_landmarks:
        # Process landmarks
        self.analyze_pose(results.pose_landmarks)

    return results

2. Accurate Angle Calculation

def calculate_angle(self, a, b, c):
    vector1 = np.array([a[0] - b[0], a[1] - b[1], a[2] - b[2]])
    vector2 = np.array([c[0] - b[0], c[1] - b[1], c[2] - b[2]])

    # Handle edge cases
    if np.linalg.norm(vector1) == 0 or np.linalg.norm(vector2) == 0:
        return 0.0

    cosine_angle = np.dot(vector1, vector2) / (
        np.linalg.norm(vector1) * np.linalg.norm(vector2)
    )
    return np.degrees(np.arccos(np.clip(cosine_angle, -1.0, 1.0)))

3. REBA Score Implementation

def calculate_reba_score(self, angles):
    # Initialize scores
    neck_score = self._get_neck_score(angles['neck'])
    trunk_score = self._get_trunk_score(angles['trunk'])
    legs_score = self._get_legs_score(angles['legs'])

    # Calculate final score
    return neck_score + trunk_score + legs_score

Lessons Learned

1. Performance Optimization
2. Use NumPy for vector calculations
3. Implement efficient angle calculations

4. Optimize frame processing

5. Error Handling
   

def safe_angle_calculation(self, landmarks):
    try:
        angles = self.calculate_angles(landmarks)
        return angles
    except Exception as e:
        self.log_error(e)
        return self.default_angles

1. Testing Strategy
2. Unit tests for calculations
3. Integration tests for video processing
4. Performance benchmarking

Results

Our implementation achieved:

• 30 FPS processing
• 95% pose detection accuracy
• Real-time REBA scoring
• Comprehensive safety alerts

Code Repository Structure

ergovision/
├── src/
│   ├── analyzer.py
│   ├── pose_detector.py
│   └── reba_calculator.py
├── tests/
│   └── test_analyzer.py
└── README.md

Future Improvements

1. Performance Enhancements

# Planned optimization
@numba.jit(nopython=True)
def optimized_angle_calculation(self, vectors):
    # Optimized computation
    pass

1. Feature Additions
2. Multi-camera support
3. Cloud integration
4. Mobile apps

Get Involved!

• Star our repository
• Try the implementation
• Contribute to development
• Share your feedback

Resources

• GitHub Repository

Happy coding! ?

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