Head-mounted eye gaze tracking devices: An overview of modern devices and recent advances

Cognolato, Matteo; Atzori, Manfredo; Müller, Henning

doi:10.1177/2055668318773991

Cited by 88 publications

(70 citation statements)

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“…The eyes are tracked using corneal reflection and dark pupil methods with automatic parallax and slippage compensation. These eye tracking data are sampled at 100 Hz with a theoretical accuracy and precision of 0.5° and 0.3° RMS 29 . Video with Full HD resolution (1920 px × 1080 px) is recorded at 25 fps by the scene camera with a horizontal and vertical field of view of approximately 82° and 52°.…”

Section: Id Agementioning

confidence: 99%

Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics

et al. 2020

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a hand amputation is a highly disabling event, having severe physical and psychological repercussions on a person's life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric hand prostheses, natural and robust control usable in everyday life is still challenging. Novel techniques have been proposed to overcome the current limitations, among them the fusion of surface electromyography with other sources of contextual information. We present a dataset to investigate the inclusion of eye tracking and first person video to provide more stable intent recognition for prosthetic control. this multimodal dataset contains surface electromyography and accelerometry of the forearm, and gaze, first person video, and inertial measurements of the head recorded from 15 transradial amputees and 30 able-bodied subjects performing grasping tasks. Besides the intended application for upper-limb prosthetics, we also foresee uses for this dataset to study eye-hand coordination in the context of psychophysics, neuroscience, and assistive robotics.

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Section: Id Agementioning

confidence: 99%

Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics

et al. 2020

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“…Eye-tracker manufacturers often limit themselves to reporting data-quality measurements for well-controlled and optimal scenarios, and many researchers simply reiterate the manufacturer's statements as applicable to their studies instead of assessing data quality of the eyetracking setup as used in their study (see e.g., Wang et al 2019;Freeth & Bugembe 2018;Hoppe, Loetscher, Morey, & Bülling, 2018). Also a recent overview of head-worn eye-tracking setups (Cognolato, Atzori, & Müller, 2018) compared eye-tracker performance based on manufacturerprovided data-quality values instead of measuring these values themselves. These practices might lead researchers to overestimate performance when choosing an eyetracking setup.…”

Section: Introductionmentioning

confidence: 99%

The impact of slippage on the data quality of head-worn eye trackers

et al. 2020

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Mobile head-worn eye trackers allow researchers to record eye-movement data as participants freely move around and interact with their surroundings. However, participant behavior may cause the eye tracker to slip on the participant's head, potentially strongly affecting data quality. To investigate how this eye-tracker slippage affects data quality, we designed experiments in which participants mimic behaviors that can cause a mobile eye tracker to move. Specifically, we investigated data quality when participants speak, make facial expressions, and move the eye tracker. Four head-worn eye-tracking setups were used: (i) Tobii Pro Glasses 2 in 50 Hz mode, (ii) SMI Eye Tracking Glasses 2.0 60 Hz, (iii) Pupil-Labs' Pupil in 3D mode, and (iv) Pupil-Labs' Pupil with the Grip gaze estimation algorithm as implemented in the EyeRecToo software. Our results show that whereas gaze estimates of the Tobii and Grip remained stable when the eye tracker moved, the other systems exhibited significant errors (0.8-3.1 • increase in gaze deviation over baseline) even for the small amounts of glasses movement that occurred during the speech and facial expressions tasks. We conclude that some of the tested eye-tracking setups may not be suitable for investigating gaze behavior when high accuracy is required, such as during face-to-face interaction scenarios. We recommend that users of mobile head-worn eye trackers perform similar tests with their setups to become aware of its characteristics. This will enable researchers to design experiments that are robust to the limitations of their particular eye-tracking setup. Keywords Head-mounted eye tracking • Wearable eye tracking • Mobile eye tracking • Eye movements • Natural behavior • Data quality Diederick C. Niehorster and Thiago Santini contributed equally and should be considered co-first authors.

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“…Various functional assessments are used to gauge the functional impact of upper limb impairment and to monitor rehabilitative progress thereafter [5], [6]. However, such assessments often do not precisely quantify hand and joint movements, grip adjustments [7], [8], or hand-eye interaction, which is recognized as an important behaviour during grasp control [9], [10]. Quantitative measurement of visuomotor behaviour collected during the execution of functional tasks can enhance the understanding of these movement features.…”

Section: Introductionmentioning

confidence: 99%

Gaze and Movement Assessment (GaMA): Inter-site validation of a visuomotor upper limb functional protocol

Williams

Chapman

Pilarski

et al. 2019

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33 Background: Successful hand-object interactions require precise hand-eye coordination with 34 continual movement adjustments. Quantitative measurement of this visuomotor behaviour could 35 provide valuable insight into upper limb impairments. The Gaze and Movement Assessment 36 (GaMA) was developed to provide protocols for simultaneous motion capture and eye tracking 37 during the administration of two functional tasks, along with data analysis methods to generate 38 standard measures of visuomotor behaviour. The objective of this study was to investigate the 39 reproducibility of the GaMA protocol across two independent groups of non-disabled participants, 40 with different raters using different motion capture and eye tracking technology.41 Methods: Twenty non-disabled adults performed the Pasta Box Task and the Cup Transfer Task.42 Upper body and eye movements were recorded using motion capture and eye tracking, 43 respectively. Measures of hand movement, angular joint kinematics, and eye gaze were compared 44 to those from a different sample of twenty non-disabled adults who had previously performed the 45 same protocol with different technology, rater and site.46 Results: Participants took longer to perform the tasks versus those from the earlier study, although 47 the relative time of each movement phase was similar. Measures that were dissimilar between the 48 groups included hand distances travelled, hand trajectories, number of movement units, eye 49 latencies, and peak angular velocities. Similarities included all hand velocity and grip aperture 50 measures, eye fixations, and most peak joint angle and range of motion measures. 51 Discussion: The reproducibility of GaMA was confirmed by this study, despite a few differences 52 introduced by learning effects, task demonstration variation, and limitations of the kinematic 53 model. The findings from this study provide confidence in the reliability of normative results 54 obtained by GaMA, indicating it accurately quantifies the typical behaviours of a non-disabled 3 55 population. This work advances the consideration for use of GaMA in populations with upper limb 56 sensorimotor impairment. 57 4 58 Introduction59 Various sensorimotor impairments including stroke [1], amputation [2], and spinal cord 60 injury [3] lead to deficits in upper limb performance that can hamper activities of daily living 61 requiring precise hand-object interactions [4]. Various functional assessments are used to gauge 62 the functional impact of upper limb impairment and to monitor rehabilitative progress thereafter 63 [5], [6]. However, such assessments often do not precisely quantify hand and joint movements, 64 grip adjustments [7], [8], or hand-eye interaction, which is recognized as an important behaviour 65 during grasp control [9], [10]. Quantitative measurement of visuomotor behaviour collected during 66 the execution of functional tasks can enhance the understanding of these movement features. 67 Measurement technologies commonly used for this purpose include eye tra...

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Head-mounted eye gaze tracking devices: An overview of modern devices and recent advances

Cited by 88 publications

References 58 publications

Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics

Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics

The impact of slippage on the data quality of head-worn eye trackers

Gaze and Movement Assessment (GaMA): Inter-site validation of a visuomotor upper limb functional protocol

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