Bi-manual Stabilization of the Cervical Spine for Safe Physical Human-Robot Interaction in Rolling Maneuvers

Elizabeth Peiros; Moira Bohley; Lucas Yager; Derek Chen; Rebecca Fellbaum; Chris D'Ambrosia; Ananya Rao; Xiao Liang

cs.ROOct 3, 2026

Bi-manual Stabilization of the Cervical Spine for Safe Physical Human-Robot Interaction in Rolling Maneuvers

Authors: Elizabeth Peiros, Moira Bohley, Lucas Yager, Derek Chen, Rebecca Fellbaum, Chris D'Ambrosia, Ananya Rao, Xiao Liang,

Organizations: Electrical and Computer Engineering Department, University of California, San Diego, La Jolla, CA 92093 USA · Mechanical and Aerospace Engineering Department, University of California, San Diego, La Jolla, CA 92093 USA · Columbia University Irving Medical Center, National Outdoor Leadership School Wilderness Medicine Institute

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.

Figures & tables

Figure 1: (a) Human performing neck stabilization task; (b) bi-manual robot platform executing neck stabilization task

Cervical Spine: Range of Motion			Max Allowable Dev
Flexion	$54.4\degree$		$10\degree$
Extension	$93.2\degree$	total $147.6\degree$	$10\degree$
Lateral Bending (L)	$40\degree$		$10\degree$
Lateral Bending (R)	$40\degree$		$10\degree$
Left Axial Rotation	$75\degree$		$10\degree$
Right Axial Rotation	$76\degree$	total $151\degree$	$10\degree$

Table I: Human model parameters: Neck Joint Limits

Frame	Attached to	Obtained from	Status
$B$	Robot base	—	Assumed Fixed
$C$	Camera	Robot FK	Constant
$M_{h}$ , $M_{t}$	ArUco markers on head, torso	Marker detection	Measured
$H$ , $Tr$	Head, torso biomechanics frames	Fixed offsets ${}^{M_{h}}T_{H}$ , ${}^{M_{t}}T_{Tr}$	Calibrated constants
$L$ , $R$	Left, right end-effectors	Forward kinematics $\mathrm{FK}(\mathbf{q})$	Measured
$F_{0}$	Torso pose at episode start	Snapshot of ${}^{B}T_{Tr}$ at $k=0$	Constant per episode

Table II: Coordinate frames.

Figure 3: Phantom used for testing which features a F/T load cell and bio-mechanically representative neck joint.

Material	$f_{y}$ (N)	Pressure (kPa)	$f_{r}$ (N)	Ratio
Ecoflex 00-10	3.0	12.43	19.57	1.574
	5.0	14.74	15.45	1.048
	7.0	16.49	14.27	0.865
Ecoflex 00-20	3.0	12.43	18.72	1.506
	5.0	14.74	18.83	1.277
	7.0	16.49	19.86	1.204

Table III: Force–pressure ratios by material

Panel of Figure 7 — Figure 7: (A) Simulated grasping and trajectory showing the head and floor collision boundaries. (B) Neck stabilization from the vantage point of the camera on the robot (C) Example neck-stabilizing sequence

Figure 8: Comparison of expert (n=21) and novice (n=56) humans to a humanoid robot with (n=12) and without (n=12) a predictive algorithm in a neck stabilization task. (A) Maximum axial rotation error of head relative to torso. (B) Max angular distance error of neck bending relative to center axial line of torso. (C) Max force exerted on neck during neck stabilization task.

Human-Robot Interaction

Human-robot interaction studies how people and robots communicate, coordinate, and safely work or live together.

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Accurate head immobilization is critical for medical procedures such as neuroimaging and intracranial radiation therapy, where even minor motion can compromise precision. Traditional methods, such as rigid stereotactic frames and thermoplastic face masks, are invasive, uncomfortable for patients, and require a time-consuming setup. To overcome these limitations, a novel approach employs a 6-degree-of-freedom (6DoF) robotic head holder that is integrated with real-time optical motion tracking, enabling dynamic compensation for head movements without physical restraint. However, implementing this system requires a detailed biomechanical understanding of skull-cervical spine interactions to define safe and effective motion limits. In this study, we conduct a human volunteer investigation using a 6DoF Stewart platform robot to characterize biokinematic workspace of the head-neck system and establish foundational data for human-robot interaction in precision head stabilization.

Abstract

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