Human-Robot Interaction Based on Gaze Gestures for the Drone Teleoperation

Yu, Mingxin; Lin, Yingzi; Schmidt, D.; Wang, Xiangzhou; Wang, Yu

doi:10.16910/jemr.7.4.4

Cited by 27 publications

(11 citation statements)

References 12 publications

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“…The usefulness and simplicity of the 9Gaze application may make it a viable and recommended new method for taking nine-directional ocular photographs. As previously reported, an automatic strabismus detection system based on the Hess chart test has been developed for use in telemedicine ( 5 ; 11 , 12 ). Incorporation of that technology with the 9Gaze application may be useful for the remote examination of patients in the future.…”

Section: Discussionmentioning

confidence: 99%

New device for taking nine-directional ocular photographs: “9Gaze” application

Goseki

Kunimi

Shioya

et al. 2022

JEMR

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This study compared the time required to produce nine-directional ocular photographs using the conventional method to that using the newly devised 9Gaze application. In total, 20 healthy adults, 10 adult patients with strabismus, and 10 pediatric patients with amblyopia or strabismus had their ocular photographs taken using a digital camera with PowerPoint 2010, and with an iPad, and iPod touch with 9Gaze. Photographs of 10 healthy patients were taken by orthoptists with <1 year of experience, and the other participants had theirs taken by those with >1 year of experience. The required time was compared between the three devices in all patients and the two orthoptist groups in 20 healthy adults (>1 year and <1 year of experience). The required times were significantly different between the devices: 515.5 ± 187.0 sec with the digital camera, 117.4 ± 17.8 sec with the iPad, and 76.3 ± 14.1 sec with the iPod touch. The required time with the digital camera was significantly different between the two orthoptist groups (404.7 ± 150.8 vs. 626.3 ± 154.2 sec, P=0.007). The use of the 9Gaze application shortened the recording time required. Furthermore, 9Gaze can be used without considering the years of experience of the examiner.

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Section: Discussionmentioning

confidence: 99%

New device for taking nine-directional ocular photographs: “9Gaze” application

Goseki

Kunimi

Shioya

et al. 2022

JEMR

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“…The analytical form of the interaction matrix for the general case is given in [6]. For the discrete case the time derivative of image moments µ ij is obtained from differentiation of (2):…”

Section: B Visual Featuresmentioning

confidence: 99%

“…Eye tracking technology provides another modality for interacting with robots in a wide range of applications. For instance, to control UAV with gaze [6] [7], to control a robotics arm [8] and in shared autonomy [9]. One of the advantages to include eye tracking technology is the possibility to increase freedom in the sense that, for instance, when a human is doing a task and he or she needs to communicate with a robot for another task, the human doesn't have to stop the current task and interacting with robots for the other task.…”

Section: Introductionmentioning

confidence: 99%

Application of Visual Servoing and Eye Tracking Glass in Human Robot Interaction: A case study

Shi

Copot

Vanlanduit

2019

2019 23rd International Conference on System Theory, Control and Computing (ICSTCC)

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Human gazes reveal a lot of information about their intention, which could be used for intuitive Human Robot Interaction(HRI). Using gaze as a modality to interact with robots has huge value in increasing freedom for human. It has a strong application potential in different domains. In this paper, we propose a system for eye tracking based HRI with which a human can select an object by his/her gaze and a robot manipulator receives the intended object and waits for the confirmation from the human before it starts the grasping task. We use Image-based Visual Servoing (IBVS) to guide the robot manipulator to perform the grasping automatically which can also bring more robustness to the system. The first hand results indicate that the use of both visual servoing features and eye tracking technique can provide successful results in term of human robot interaction.

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“…Using gaze gestures is an effective hands-free modality for human-computer interaction (HCI) and human-robot interaction (HRI). The application of gaze gestures in HCI/HRI includes controlling a camera (Fujii et al, 2018), authentication (Findling et al, 2019), guiding a drone (Yu et al, 2014), text input (Wobbrock et al, 2008), and so on. Various approaches have been proposed for gaze gesture recognition.…”

Section: Introductionmentioning

confidence: 99%

Gaze Gesture Recognition by Graph Convolutional Networks

2021

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Gaze gestures are extensively used in the interactions with agents/computers/robots. Either remote eye tracking devices or head-mounted devices (HMDs) have the advantage of hands-free during the interaction. Previous studies have demonstrated the success of applying machine learning techniques for gaze gesture recognition. More recently, graph neural networks (GNNs) have shown great potential applications in several research areas such as image classification, action recognition, and text classification. However, GNNs are less applied in eye tracking researches. In this work, we propose a graph convolutional network (GCN)–based model for gaze gesture recognition. We train and evaluate the GCN model on the HideMyGaze! dataset. The results show that the accuracy, precision, and recall of the GCN model are 97.62%, 97.18%, and 98.46%, respectively, which are higher than the other compared conventional machine learning algorithms, the artificial neural network (ANN) and the convolutional neural network (CNN).

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Human-Robot Interaction Based on Gaze Gestures for the Drone Teleoperation

Cited by 27 publications

References 12 publications

New device for taking nine-directional ocular photographs: “9Gaze” application

New device for taking nine-directional ocular photographs: “9Gaze” application

Application of Visual Servoing and Eye Tracking Glass in Human Robot Interaction: A case study

Gaze Gesture Recognition by Graph Convolutional Networks

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