Designed and implemented a real-time posture assessment system using MediaPipe and YOLO for pose estimation, demonstrating strong computer vision skills. Evaluated ergonomic risks through RULA and REBA scoring, providing actionable insights to prevent work-related musculoskeletal disorders and enhance workplace safety. This is a practical representation of Computer Vision in a real-life problem, it makes a starking attempt to help the companies and individuals in ergonomics and personal safety.
- Improve Workplace Safety: Deploy a real-time posture assessment system using MediaPipe and YOLO to reduce work-related musculoskeletal disorders.
- Enhance Ergonomic Risk Evaluation: Utilize RULA and REBA scoring integrated with computer vision technologies for accurate ergonomic risk assessments.
- Promote Proactive Interventions: Provide data-driven insights to educate workers and management on ergonomic hazards, fostering a safer work environment.
Computer Vision and Pose Estimation:
MediaPipe:
Used for human pose estimation to detect and analyze body posture in real-time.
YOLO (You Only Look Once):
Employed for object detection and enhancing the accuracy of pose estimation.
Ergonomic Risk Evaluation:
RULA (Rapid Upper Limb Assessment):
Algorithm implemented for assessing upper limb ergonomic risks based on pose data.
REBA (Rapid Entire Body Assessment):
Algorithm used to evaluate the whole body ergonomic risks from the pose data.
Frameworks and Libraries:
OpenCV:
Used for image processing and computer vision tasks.
NumPy:
Utilized for numerical operations and data manipulation.
Pandas:
Used for data analysis and handling ergonomic scoring results.
#Future Scope
- Integration with Wearable Technology
- Machine Learning for Predictive Analytics