Compensation of head movements in mobile eye-tracking data using an inertial measurement unit

Larsson, Linnéa; Schwaller, Andrea; Holmqvist, Kenneth; Nyström, Maria; Martin, Stridh

doi:10.1145/2638728.2641693

Cited by 15 publications

(8 citation statements)

References 6 publications

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“…The need to detect fixations as early as possible therefore implies a detection method that uses more information than eye movement alone. Whether this is best done by compensating for head movements (Kinsman et al, 2012; Larsson et al, 2014) or by comparing the visual object at the gaze point as in the present study is an open question.…”

Section: Discussionmentioning

confidence: 91%

On the Visuomotor Behavior of Amputees and Able-Bodied People During Grasping

Gregori¹,

Cognolato²,

Saetta³

et al. 2019

Front. Bioeng. Biotechnol.

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Visual attention is often predictive for future actions in humans. In manipulation tasks, the eyes tend to fixate an object of interest even before the reach-to-grasp is initiated. Some recent studies have proposed to exploit this anticipatory gaze behavior to improve the control of dexterous upper limb prostheses. This requires a detailed understanding of visuomotor coordination to determine in which temporal window gaze may provide helpful information. In this paper, we verify and quantify the gaze and motor behavior of 14 transradial amputees who were asked to grasp and manipulate common household objects with their missing limb. For comparison, we also include data from 30 able-bodied subjects who executed the same protocol with their right arm. The dataset contains gaze, first person video, angular velocities of the head, and electromyography and accelerometry of the forearm. To analyze the large amount of video, we developed a procedure based on recent deep learning methods to automatically detect and segment all objects of interest. This allowed us to accurately determine the pixel distances between the gaze point, the target object, and the limb in each individual frame. Our analysis shows a clear coordination between the eyes and the limb in the reach-to-grasp phase, confirming that both intact and amputated subjects precede the grasp with their eyes by more than 500 ms. Furthermore, we note that the gaze behavior of amputees was remarkably similar to that of the able-bodied control group, despite their inability to physically manipulate the objects.

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Section: Discussionmentioning

confidence: 91%

On the Visuomotor Behavior of Amputees and Able-Bodied People During Grasping

Gregori¹,

Cognolato²,

Saetta³

et al. 2019

Front. Bioeng. Biotechnol.

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“…Kugler et al ( 2014 ) likely avoided these problems because the recording time was very short. In a similar manner, Larsson et al (2014) proposed the integration of a mobile eye tracking system with one inertial measurement unit (IMU) for head movement compensation in an indoor scenario, again avoiding the many problems of sensor drift in outdoor recordings.…”

Section: Introductionmentioning

confidence: 99%

“…We present a novel approach for tracking gaze relative to the heading direction while walking outdoors realized via a portable system comprised of a head mounted eye tracker and a pair of off-the-shelf motion sensors for head movement tracking. In contrast to the mobile tracking devices implemented by Kugler et al ( 2014 ) and Larsson et al ( 2014 ), which used only one motion sensor, we use a pair of motion sensors to address the problem of sensor drift that occurs during extended periods of outdoor recording. The system was designed to measure gaze and head scanning behaviors of visually impaired people as well as normally sighted people, and was tested on busy city streets in order to demonstrate its potential use for addressing the challenges in recording and analyzing human gaze patterns in outdoor environments.…”

Section: Introductionmentioning

confidence: 99%

Mobile gaze tracking system for outdoor walking behavioral studies

et al. 2016

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Most gaze tracking techniques estimate gaze points on screens, on scene images, or in confined spaces. Tracking of gaze in open-world coordinates, especially in walking situations, has rarely been addressed. We use a head-mounted eye tracker combined with two inertial measurement units (IMU) to track gaze orientation relative to the heading direction in outdoor walking. Head movements relative to the body are measured by the difference in output between the IMUs on the head and body trunk. The use of the IMU pair reduces the impact of environmental interference on each sensor. The system was tested in busy urban areas and allowed drift compensation for long (up to 18 min) gaze recording. Comparison with ground truth revealed an average error of 3.3° while walking straight segments. The range of gaze scanning in walking is frequently larger than the estimation error by about one order of magnitude. Our proposed method was also tested with real cases of natural walking and it was found to be suitable for the evaluation of gaze behaviors in outdoor environments.

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“…Visual fixations of objects will result in small or nonsignificant rotation of the eyeball during stationary motion phases, see Figure 1 a. A rotation of the head during visual fixation of a stationary object however, will result in an opposite rotation of the eyeball due to the vestibo-ocular reflex, stabilizing the visual scenery [ 20 ], see Figure 1 b. The physiological relationship between head and eye rotations therefore represents a natural indicator for head rotation and can be used to support head orientation measurements.…”

Section: Working Principlementioning

confidence: 99%

SteadEye-Head—Improving MARG-Sensor Based Head Orientation Measurements Through Eye Tracking Data

Wöhle¹,

Gebhard²

2020

Sensors

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This paper presents the use of eye tracking data in Magnetic AngularRate Gravity (MARG)-sensor based head orientation estimation. The approach presented here can be deployed in any motion measurement that includes MARG and eye tracking sensors (e.g., rehabilitation robotics or medical diagnostics). The challenge in these mostly indoor applications is the presence of magnetic field disturbances at the location of the MARG-sensor. In this work, eye tracking data (visual fixations) are used to enable zero orientation change updates in the MARG-sensor data fusion chain. The approach is based on a MARG-sensor data fusion filter, an online visual fixation detection algorithm as well as a dynamic angular rate threshold estimation for low latency and adaptive head motion noise parameterization. In this work we use an adaptation of Madgwicks gradient descent filter for MARG-sensor data fusion, but the approach could be used with any other data fusion process. The presented approach does not rely on additional stationary or local environmental references and is therefore self-contained. The proposed system is benchmarked against a Qualisys motion capture system, a gold standard in human motion analysis, showing improved heading accuracy for the MARG-sensor data fusion up to a factor of 0.5 while magnetic disturbance is present.

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Compensation of head movements in mobile eye-tracking data using an inertial measurement unit

Cited by 15 publications

References 6 publications

On the Visuomotor Behavior of Amputees and Able-Bodied People During Grasping

On the Visuomotor Behavior of Amputees and Able-Bodied People During Grasping

Mobile gaze tracking system for outdoor walking behavioral studies

SteadEye-Head—Improving MARG-Sensor Based Head Orientation Measurements Through Eye Tracking Data

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