Gerald Zwettler
Papers
2
Total Citations
3
H-Index
1
About
Gerald Zwettler’s research bridges computer vision, deep learning, and human-robot interaction, with a focus on precise orientation-estimation and socially assistive robotics. His most cited work, “Hybrid Approach for Orientation-Estimation of Rotating Humans in Video Frames Acquired by Stationary Monocular Camera” (2020), tackles the challenging problem of estimating human body orientation from a single camera—a task complicated by camera calibration and the deformable nature of moving bodies. By integrating deep learning techniques, Zwettler advances robust object pose-estimation for applications in surveillance, sports analytics, and human-robot collaboration. His more recent contribution, “Appearance Matters: Insights from Co-Design and Evaluation of Social Assistive Robots” (2025), explores how robot aesthetics influence user trust and engagement, offering practical guidelines for designing socially acceptable robots. Though his citation counts are modest, Zwettler’s work demonstrates a commitment to solving real-world problems at the intersection of perception and interaction. His hybrid methodologies and user-centered design insights provide a foundation for future research in autonomous systems and assistive technology, making him a thoughtful contributor to these evolving fields.
Research Focus
Key Achievements
Top Papers
- 1
- 2