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:
- Real-time processing requirements
- Accurate pose estimation
- Professional safety standards
- 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
- Performance Optimization
- Use NumPy for vector calculations
- Implement efficient angle calculations
Optimize frame processing
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
- Testing Strategy
- Unit tests for calculations
- Integration tests for video processing
- 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
- Performance Enhancements
# Planned optimization
@numba.jit(nopython=True)
def optimized_angle_calculation(self, vectors):
# Optimized computation
pass
- Feature Additions
- Multi-camera support
- Cloud integration
- Mobile apps
Get Involved!
- Star our repository
- Try the implementation
- Contribute to development
- Share your feedback
Resources
Happy coding! 🚀
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