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About

Christopher Funk is a researcher at the intersection of cognitive robotics and biomechanical AI, whose work bridges the gap between how machines see and how they understand physical human dynamics. His early research pioneered a cognitive vision system for mobile robots that mimics the human visual system—employing saccadic, vergence, and pursuit movements to build detailed 3D models of small regions at each fixation. This biologically-inspired approach laid the groundwork for his later, more impactful contributions. Funk’s most significant work comes from his innovative application of deep learning to biomechanics. He developed PressNet and PressNet-Simple, end-to-end architectures that can regress 2D foot pressure heatmaps and estimate the Center of Pressure (CoP) directly from standard video frames of human motion. This breakthrough allows researchers to infer dynamic forces from kinematic data—a task traditionally requiring specialized force plates. With over 35 combined citations for his key papers, Funk’s work has direct applications in kinesiology, postural control analysis, gait studies, and rehabilitation medicine. His research represents a novel convergence of computer vision, robotics, and biomechanics, demonstrating how virtual world planning and cognitive architectures can be extended to understand and analyze human physical dynamics in the real world.