Determination of the Human-Robot Interaction Workspace for Robot Assisted Head Stabilization Control
Authors: Xinmin Liu, Alexander L. Van Slyke, Destinee Holloman, Ahmad Sakaamini, Rodney D. Wiersma
Organizations: Department of Radiation Oncology, University of California at Los Angeles, CA · Department of Radiation Oncology, Yale New Haven Hospital, Trumbull, CT · University of Colorado, Anschutz Medical Campus, Aurora, CO · Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA
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.
Figures & tables
Fig. 1: SRS immobilization. Top row: Traditional frame-based SRS setup where a metal ring is fixed to the patient’s skull using four screws 8 . Bottom row: Frameless SRS immobilization. A triple mask configuration to minimize head movement: (1) bottom mask, (2) intermediate mask with chin strap, and (3) top masks 22 .
Fig. 2: Robotic SRS prototype head motion stabilization for volunteers using IR marker tracking head motions. Camera inputs are automatically transformed and 6DoF errors are determined. A cancellation trajectory is calculated, and motion to accomplish cranial stabilization is transmitted to the robot’s linear actuators.
Fig. 3: A configuration of robotic head support with four plastic posts for facilitating head rotational control with the robot. A simple pillow support with only contact in the occipital region would not generate enough torque to rotate the head.
Fig. 4: Different coordinate frames used for radiation delivering and position measuring for a patient in a LINAC system. Left: A LINAC machine shown in a treatment room (image source: https://radformation.com). Middle: the coordinate frames related to the IR camera that tracks the patient’s position are displayed. Right: the coordinate frame of a human lying on their back is defined.
Fig. 5: Calibrating the IR Camera frame to the LINAC frame. Top: calibrating the orientation of two frames with translational motion. Bottom: determining translational offset of two frames with rotational motion.
Fig. 6: Target tracking by using IR tool. Top: During volunteer setup, the mock target on the volunteer’s head is aligned with the LINAC frame. Bottom: The displacement of the target is monitored in the LINAC frame using the IR tool’s 6D position tracking.
Fig. 7: Robot rotated around different target points in human head
Fig. 8: The measured virtual target cervical vertebrae C1 trajectory followed the desired pure translational and rotational motion.
Fig. 9: Volunteer head reaction to robot translational and rotational motion. A) corresponded to one volunteer with different motion type (left-right, superior-inferior, and anterior-posterior) indicated by columns. Along each direction, the particular robot motion was repeated three times. Similarly, B) corresponded to one volunteer with different motion type (pitch, roll and yaw).
x
y
z
pitch
roll
yaw
Volunteer1
11.60 ± 0.68
1.75 ± 0.84
10.97 ± 0.72
7.79 ± 0.86
10.21 ± 0.88
3.63 ± 0.85
Volunteer2
10.60 ± 0.63
1.67 ± 0.65
11.01 ± 0.52
6.27 ± 0.74
9.20 ± 0.20
2.93 ± 0.18
Volunteer3
10.86 ± 0.40
2.47 ± 0.31
10.56 ± 1.51
8.75 ± 0.71
10.30 ± 0.99
4.40 ± 0.54
TABLE I: Volunteers motion range with respect to robot motion (x:14mm, y:8mm, z:14mm, pitch:10deg, roll:10deg and yaw:6deg)
Fig. 10: Boxplot of the ranges of correponding volunteers reaction to individual robot motion.
Fig. 11: Sample data for one volunteer displaying dimensional coupling in head motion resulting from robot motion along a particular direction. (A) shows the volunteer’s reaction to translational robot motion, while (B) shows reaction to rotational motion.
Fig. 12: Human motion around different tumor target positions. One was in cervical vertebrae C1, and the other was [-60mm, 120mm, 60mm] away from C1. The left boxplot in each pair was corresponding to robot motion moving around C1.
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.
Elizabeth Peiros, Moira Bohley, Lucas Yager +6
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
Humanoid robots have become increasingly popular in applications such as social interaction, education, and service roles, which drives the need for more natural and efficient human-robot interactions. However, currently available humanoid heads often face limitations, including high costs, mechanical complexity, and limited adaptability across diverse environments. To address these challenges, we present an articulated humanoid robot head designed for a receptionist role, integrating a mechanical structure with 21 degrees of freedom (DoF), including mechanisms for the mouth, eyes, eyebrows, and neck, and covered with realistic silicone skin to achieve a human-like appearance and expression. The system integrates a model-based architecture that combines SCRFD, ArcFace, and ByTetrack for face recognition and Llama and Whisper for natural language processing, with hardware support enabling real-time operations and human re-identification. The conversational ability and re-identification capabilities of the humanoid robot head were quantitatively measured, while its emotional expressiveness and human likeness were evaluated through a user study, achieving an average human likeness score of 4.13 out of 5.
Manual craniotomy is a high-risk, skill-dependent procedure associated with surgeon fatigue and potential dural injury. While robotic approaches have improved safety, existing open-loop systems rely solely on preoperative images and cannot compensate for intraoperative registration errors or tissue deformation. To address this, we propose a human-inspired closed-loop robotic craniotomy framework that intelligently integrates preoperative planning with intraoperative execution. An adaptive dual-contour fusion algorithm is employed to generate trajectories that conform to complex cranial geometries while maintaining a consistent tool-bone relative pose. For intraoperative perception, a multimodal two-stage cross-modal attention block (CMA)-temporal convolutional network (TCN)-Transformer network combined with an adaptive Bayesian filter fuses force and acoustic signals to achieve robust breakthrough detection under varying bone conditions. Upon detection, an in-situ projection-based trajectory adjustment strategy dynamically compensates for depth deviations, enabling safe residual bone isolation. Experiments on bovine ribs show a breakthrough prediction accuracy of 97%, a detection latency of 0.048 +/- 0.097 s, and a maximum overshoot of 0.29 mm. All four ex vivo cranial experiments were successfully completed without dural injury. These results demonstrate that the proposed cybernetic framework enables safe and autonomous craniotomy with highly effective closed-loop control.
Renzhen Le, Xiao Zhang, Di Wu +5
Dalian University of Technology, Dalian 116024, China · Department of Mechanical Engineering, The University of Tokyo, Tokyo 113-8656, Japan