Paula Amorim
Papers
2
Total Citations
18
H-Index
2
About
Paula Amorim is a researcher at the intersection of computer vision, assistive robotics, and human motion analysis. Her work focuses on developing intelligent systems that can detect and interpret changes in human gait patterns, with direct applications in healthcare monitoring and robotic assistance. In her most cited paper, "Human gait pattern changes detection system: A multimodal vision-based and novelty detection learning approach" (2017, 12 citations), she introduced a novel framework that combines multiple visual modalities with machine learning to identify subtle shifts in walking patterns. She further advanced this line of inquiry in "Trajectory-based gait pattern shift detection for assistive robotics applications" (2019, 6 citations), where she applied trajectory analysis to enable real-time adaptation in assistive robots. Though her citation counts are modest, Amorim’s work is foundational in the niche but growing field of gait-based human-robot interaction, offering practical pathways for early detection of mobility impairments and personalized robotic support. Her research exemplifies how vision-based novelty detection can bridge the gap between raw sensor data and meaningful clinical or robotic interventions.
Research Focus
Key Achievements
Top Papers
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