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This research applies advanced robotics control algorithms to solve a critical safety problem in physical human-robot interaction, specifically stabilizing the cervical spine during patient transport maneuvers. By achieving near-human precision in neck rotation alignment, the study demonstrates how AI-driven motion planning can directly enhance safety protocols in high-risk environments like disaster zones and battlefields.
Bi-manual Stabilization of the Cervical Spine for Safe Physical Human-Robot Interaction in Rolling Maneuvers
arXiv:2610.04220v1 Announce Type: new Abstract: The neck, specifically the cervical spine, is a highly fragile and mobile part of the body with a high risk of catastrophic injury. Whether someone is injured on a battlefield, in a natural disaster, or in an athletic event, great care is always taken with the cervical spine when transporting, rolling, or moving the patient. In this work, we present an algorithm and adaptations for bi-manual robots to stabilize the cervical spine during rolling maneuvers. We investigate techniques to maximize grip strength within safety bounds and present a control approach for proper neck rotation. We found that with our approach, a bi-manual robot is capable of achieving human-level performance in maintaining target rotational alignments. The axial alignment for the robot with prediction was within .21 degrees of human novices.
Abstract:The neck, specifically the cervical spine, is a highly fragile and mobile part of the body with a high risk of catastrophic injury. Whether someone is injured on a battlefield, in a natural disaster, or in an athletic event, great care is always taken with the cervical spine when transporting, rolling, or moving the patient. In this work, we present an algorithm and adaptations for bi-manual robots to stabilize the cervical spine during rolling maneuvers. We investigate techniques to maximize grip strength within safety bounds and present a control approach for proper neck rotation. We found that with our approach, a bi-manual robot is capable of achieving human-level performance in maintaining target rotational alignments. The axial alignment for the robot with prediction was within .21 degrees of human novices.
Robotics (cs.RO)
arXiv:2610.04220 [cs.RO]
(or arXiv:2610.04220v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.04220 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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