Joel Quarnstrom
Papers
2
Total Citations
11
H-Index
2
About
Joel Quarnstrom is an emerging researcher specializing in human-robot interaction, biomechanics, and collaborative robotics. His work focuses on the critical intersection of human motion prediction and robotic systems, with particular emphasis on developing accurate computational models that enable safer and more efficient human-robot collaboration in physically demanding tasks. Quarnstrom's most notable contributions include pioneering research into human-robot collaborative lifting, where he has developed sophisticated predictive models validated through rigorous experimentation. His 2023 paper on collaborative lifting motion prediction has already garnered 7 citations, demonstrating rapid uptake in the robotics community. Complementing this, his 2022 investigation into grasping force prediction introduced an innovative 13 degrees-of-freedom three-dimensional human arm model alongside a 10 DOF robotic arm model, utilizing Denavit-Hartenberg representation to bridge the gap between human biomechanics and robotic systems design. With a combined citation count reflecting growing recognition in his field, Quarnstrom's research holds significant implications for workplace ergonomics, assistive robotics, and industrial automation. His methodological approach — blending biomechanical modeling with experimental validation — positions him as a promising voice in the advancement of safe, intuitive human-robot physical collaboration.
Research Focus
Key Achievements
Top Papers
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