Carolina Pacheco

Papers

Total Citations

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About

Carolina Pacheco’s research bridges the frontiers of computer vision, robotics, and control systems, with a particular focus on infant activity recognition and medical applications. Her most cited work, “A Detection-based Approach to Multiview Action Classification in Infants” (2021, 11 citations), addresses the unique challenges of analyzing children’s movements—distinct from adults due to differing body proportions and poses—for applications in safety monitoring, behavioral assessment, and child-robot interaction. This contribution is vital for developing responsive, child-aware technologies. Pacheco also explores advanced control theory, as seen in “Fractional-order State Observers for Integer-Order Linear Systems” (2017, 2 citations), which has implications for precise robotic systems, including robot-assisted surgery. In this domain, her work on extracting femoral head-neck orientation from CT scans using image processing demonstrates a practical intersection of control and medical imaging. While her citation counts are modest, Pacheco’s research is notable for its interdisciplinary reach—combining machine learning, control theory, and healthcare—and for tackling the underexplored area of infant activity recognition. Her work holds promise for safer, more intuitive human-robot interactions and improved surgical outcomes.