A Biomimetic Gaze Control to Restore Head-Neck Movements
Authors: Colin Rubow, Haohan Zhang
Organizations: Department of Mechanical Engineering and Robotics Center, University of Utah, Salt Lake City, UT 84112, USA. · Rocky Mountain Center for Occupational and Environmental Health, University of Utah, Salt Lake City, UT 84112, USA.
Powered neck exoskeletons are developed to restore head-neck motions for those with neck muscle weakness. Current methods using hand-held input devices to control these robotic systems are unintuitive or non-viable for these populations. Alternatively, the eyes coordinate with the head to accomplish many daily tasks. Therefore, movements of the eyes can be a useful signal to control the neck exoskeletons without needing to use the hands. However, previous control models lack biobehavioral basis as well as means to personalize the control behavior, causing unnatural head-neck movements. This paper presents a novel eye-tracking controller design leveraging the vestibulo-ocular reflex to emulate natural eyehead coordination. Personalization tools were also designed, consisting of sliders to allow users to adjust the parameters of this controller. Here, we present the technical design and validation of this system in human users. Compared to the previous eye-tracking model, the new controller provides natural head motions personalized to the user through more natural velocity profiles and a 24 percent average increase of accurate motion intent detection. This system provides a powerful platform for studying eye-tracking controllers in the context of personalization and preference, and ultimately restoring the lost head-neck mobility in patient populations.
Figures & tables
Fig. 1: Deadzone and gaze diagram with plot of rotational speed related to gaze shift distance. Δθdes is the desired angle of the rotation of the head. θ˙h is the velocity of the head which is broken into its pitch and yaw rotational components. dx and dy are the lengths of the deadzone elliptical axis where the deadzone is centered in the world camera view. While the gaze is inside the deadzone, the head velocity is 0, but once outside the deadzone, the velocity increases linearly at the rate of vp , the velocity proportion, in the direction of the gaze.
Fig. 2: Trajectory comparison results between the vector and region-based controllers in 100 computer simulated gaze shifts. (left) A typical gaze shift performance. The vector controller moves straight towards the target, while the region-based controller moves first to the right, then zigzags towards the target (enlarged in the zoomed view). Both controllers stop when the gaze target hits their respective deadzones, the dashed lines of the same color. The 100 gaze targets are distributed from directly rightward to upwards with a slope of 45 ∘ . (right) Box plot of the difference of path lengths between both controllers. Higher values correspond to a higher path length performed by the region-based controller as compared to the vector controller. The median value is 13.7∘ .
Fig. 3: The robotic system. The arrows denote the directional interaction subsystems have with each other. The android phone runs a program that allows gaze data streaming.
Fig. 4: The powered neck exoskeleton with eye-tracking integration. An inertial measurement unit (IMU) was included for the study as an independent measurement of head motions, but it was not used in the robot control. The headband can be easily detached from the rest of the robot by removing a set of fasteners.
Fig. 5: Mechanical sliders for manual parameter tuning. Stops restrict linear potentiometers to their linear region. The sliders consist of rails and low-friction rollers.
Fig. 6: Setup for the eye-head coordination model justification experiment. The IMU and eye-trackers were attached to the participant with a form fitting headband. The reflection blocking screen wrapped around the experiment space to block distracting motions and reflections.
Were the sliders smooth and easy to manipulate?
Do you feel like you could easily explore the various possibilities of the parameters?
Was having three parameters to change too confusing?
Do you feel like you were able to find an acceptable tuning of parameters?
TABLE I: survey questions after the third part of the experiment
Velocity Proportion Regression
Deadzone Best Fit Ellipse
Participant
Slope ( radrad/s )
R-Value
Horizontal Diameter ( ∘ )
Vertical Diameter ( ∘ )
Classification Error (%)
1
2.298
0.946
53.16
55.53
26.50
2
2.752
0.928
94.47
75.12
9.50
3
1.941
0.926
7.10
77.27
16.00
4
2.346
0.877
20.88
92.34
15.00
5
2.221
0.767
19.76
49.00
13.00
TABLE II: natural eye-head characteristics measured in the deadzone model justification experiment
Fig. 7: Natural characteristics for participant 4. (left) Deadzone best-fit ellipse. The squares and dots respectively represent the non head moving and head moving targets evenly distributed across the screen. Target locations relative to the center of the screen are measured in inches. The red ellipse is the best-fit ellipse projected onto the screen. Its axis lengths are reported along with its gaze target classification error. (right) Velocity proportion regression. Each sample represents a single head moving gaze shift. The red line is the regression result whose slope is the velocity proportion.
Fig. 8: Difference of classification errors for the vector and region-based deadzones with mean errors of 16.81 and 40.44 percent respectively. A Welch’s t-test gives a p-value of 0.014 indicating the difference is significant.
Deadzone Ellipse
Velocity Proportion Regression
Absolute Mean(std) Difference End Orientation
Participant
Performance Error (%)
Slope ( radrad/s )
R-Value
Bending ( ∘ )
Flexion ( ∘ )
Rotation ( ∘ )
1
4.0
1.801
0.958
3.963
(3.757)
2.484
(2.764)
7.451
(6.926)
2
14.5
1.980
0.971
4.869
(4.317)
1.626
(1.534)
4.762
(4.831)
3
3.5
2.045
0.926
6.501
(4.065)
5.408
(4.655)
11.183
(13.426)
4
4.0
2.355
0.965
6.726
(4.482)
3.987
(3.937)
8.750
(7.627)
5
5.5
2.005
0.924
5.544
(3.920)
6.258
(5.910)
9.098
(10.685)
TABLE III: performance measures of the exoskeleton from the second part of the experiment
Fig. 9: Gaze target classification and robot actuation in the world view camera for participant 4 during the second part of the experiment. Gaze targets are no longer in a grid due to errors such as camera distortion. Target locations in the world camera are measured in pixels. The red ellipse is the natural deadzone projected onto the world camera view. Its axis lengths are reported along with its respective error. (left) Controller classification of gaze targets based on relative position of the deadzone in software. Note that classification is perfect. (right) Actuation performance of the robot in correctly moving or not moving according to controller decisions. Note the robot incorrectly didn’t actuated for some gaze targets.
Fig. 10: Convergent tuning of parameters for participant 2. The participant explored till the 4th trial, then converged the parameters until the 12th trial. After convergence, the participant explored further. The decorators on the y-axis indicate the maximum tunable values. The deadzone parameters are tuned by the two degree-of-freedom Cartesian system and the velocity proportion parameter is tuned by the one degree-of-freedom system. There are 16 trials.
Fig. 11: Controller parameter values obtained from the sliders subtracted by the values obtained by the measurements from the first part of the experiment (Table II ). The difference of velocity proportion are shown on the left ( vp ). The deadzone horizontal and vertical diameters are shown on the right ( θdx,θdy ) . The mean absolute values from left to right are 0.9 radians per second per radian, 44 ∘ , and 46 ∘ .
Dropped head syndrome, caused by neck muscle weakness from neurological diseases, severely impairs an individual's ability to support and move their head, causing pain and making everyday tasks challenging. Our long-term goal is to develop an assistive powered neck exoskeleton that restores natural movement. However, predicting a user's intended head movement remains a key challenge. We leverage virtual reality (VR) to collect coupled eye and head movement data from healthy individuals to train models capable of predicting head movement based solely on eye gaze. We also propose a novel multi-layer controller selection framework, where head control strategies are evaluated across decreasing levels of abstraction -- from simulation and VR to a physical neck exoskeleton. This pipeline effectively rejects poor-performing controllers early, identifying two novel gaze-driven models that achieve strong performance when deployed on the physical exoskeleton. Our results reveal that no single controller is universally preferred, highlighting the necessity for personalization in gaze-driven assistive control. Our work demonstrates the utility of VR-based evaluation for accelerating the development of intuitive, safe, and personalized assistive robots.
Colin Rubow, Eric Brewer, Ian Bales +2
Dept. of Mechanical Engineering, University of Utah, UT 84112, USA. · Robotics Center, University of Utah, UT 84112, USA. · Kahlert School of Computing, University of Utah, UT 84112, USA.
Wearable exosuits assist human movement in tasks ranging from rehabilitation to daily activities; specifically, head-neck support is necessary for patients with certain neurological disorders. Rigid-link exoskeletons have shown to enable head-neck mobility compared to static braces, but their bulkiness and restrictive structure inspire designs using "soft" actuation methods. In this paper, we propose a fabric pneumatic artificial muscle-based exosuit design for head-neck support. We describe the design of our prototype and physics-based model, enabling us to derive actuator pressures required to compensate for gravitational load. Our modeled range of motion and workspace analysis indicate that the limited actuator lengths impose slight limitations (83% workspace coverage), and gravity compensation imposes a more significant limitation (43% workspace coverage). We introduce compression force along the neck as a novel, potentially comfort-related metric. We further apply our model to compare the torque output of various actuator placement configurations, allowing us to select a design with stability in lateral deviation and high axial rotation torques. The model correctly predicts trends in measured data where wrapping the actuators around the neck is not a significant factor. Our test dummy and human user demonstration confirm that the exosuit can provide functional head support and trajectory tracking, underscoring the potential of artificial muscle-based soft actuation for head-neck mobility assistance.
Katalin Schäffer, Ian Bales, Haohan Zhang +1
Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA. · Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary. · Department of Mechanical Engineering and Robotics Center, University of Utah, Salt Lake City, UT 84112 USA.
Goal-directed eye movements are a fundamental component of visuomotor control, enabling humans to anticipate and guide their actions. Whether this anticipatory and task-driven behavior is preserved when actions are executed through a robot rather than through one's own body remains unclear. Here we address this question by investigating gaze behavior during goal-directed telemanipulation to determine how visuomotor control adapts to altered embodiment. Our findings show that gaze remains strongly aligned with task goals, preserving its predictive role even during robot-mediated manipulation. At the same time, teleoperation systematically redistributes visual attention toward the robotic end-effector and manipulated objects, increasing online monitoring. These findings show that predictive gaze is not lost under altered embodiment, but reorganized in response to changes in sensory feedback and control demands. More broadly, they reveal the flexibility of the human visuomotor system when the natural sensorimotor coupling is disrupted and identify gaze as an informative signal for inferring action intentions in human-robot interaction.
Manuela Uliano, Silvia Fattorini, Marco Controzzi
The BioRobotics Institute, Scuola Superiore Sant’Anna; Pisa, Italy · Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna; Pisa, Italy