Papers
32
Total Citations
918
H-Index
14
About
Emanuele Ruffaldi is a prolific researcher whose work spans human motion tracking, human-robot interaction, teleoperation, and technology-enhanced learning. His most influential contribution, a comprehensive survey on inertial sensor-based motion tracking (355 citations), has become an essential reference for researchers exploring cost-effective alternatives to optical motion capture, particularly for upper limb analysis. Complementing this, his 2013 work introducing a novel 7-degrees-of-freedom kinematic model for wearable sensor-based arm reconstruction further solidified his expertise in biomechanical modeling. Ruffaldi has made significant strides in robotic teleoperation and augmented reality, demonstrating how immersive interfaces and AR overlays can dramatically improve operator performance in remote manipulation tasks — research that has garnered over 200 collective citations. His ROS-integrated teleoperation framework (72 citations) exemplifies his commitment to practical, deployable human-robot systems. Beyond robotics, he has explored rehabilitation exoskeletons, telemedicine haptics, and multimodal learning analytics, revealing a uniquely interdisciplinary vision. His work on AR-enhanced robot intent visualization and motion retargeting to humanoid platforms further underscores his broad reach. Across these domains, Ruffaldi consistently bridges fundamental research and real-world application, making substantial contributions to how humans and intelligent systems collaborate and communicate.
Research Focus
Key Achievements
Top Papers
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- 3Immersive ROS-integrated framework for robot teleoperation72 citations · 2015
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- 6Third Point of View Augmented Reality for Robot Intentions Visualization35 citations · 2016
- 7Haptic guidance of Light-Exoskeleton for arm-rehabilitation tasks30 citations · 2009
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- 10Multi-contact motion retargeting from human to humanoid robot20 citations · 2016