Robotic Mimicry Leveraging Posenet Software for Human-Like Movements

This research presents a novel algorithm for accurate pose estimation tailored for human posture mimicry in robotics, optimized for low-cost and resource-constrained embedded systems. Integrating Convolutional Neural Network (CNN) PoseNet for object detection with stereo correspondences for spatial reconstruction, the algorithm enables precise estimation of human posture. Leveraging ORB features and weighted averaging of stereo-corresponded key points, it achieves superior accuracy while maintaining resource efficiency. Comparative testing against deep learning-based methods demonstrates its efficacy for robotic applications. The algorithm's affordability and efficiency make it well-suited for implementing human posture mimicry in robotics, with potential applications in personalized rehabilitation and assistive technology. In conclusion, this research presents a versatile and accessible solution for advancing human-robot interaction through posture mimicry.