Controlling 3-D Movement of Robot Manipulator using Electrooculography

Rusydi, Muhammad Ilhamdi; Okamoto, Takeo; Ito, Satoshi; Sasaki, Minoru

doi:10.15676/ijeei.2018.10.1.12

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…Moreover, the method has also been constructed using the positive and negative states of the amplitude of the two channels of AC EOG signals. In robotic applications, Ilhamdi et al [24,25] have applied EOG gaze estimation to 3D robot arm control by installing two cameras on the side and top of the target object. The processes include (1) the sagittal plane and position control of the arm tip via EOG gaze estimation based on the side camera image; (2) display switching via eye blink discrimination based on EOG; and (3) horizontal plane and position control via EOG gaze estimation based on the top camera image, allowing object pointing to be realized by the robot arm.…”

Section: Introductionmentioning

confidence: 99%

Object Grasp Control of a 3D Robot Arm by Combining EOG Gaze Estimation and Camera-Based Object Recognition

et al. 2023

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The purpose of this paper is to quickly and stably achieve grasping objects with a 3D robot arm controlled by electrooculography (EOG) signals. A EOG signal is a biological signal generated when the eyeballs move, leading to gaze estimation. In conventional research, gaze estimation has been used to control a 3D robot arm for welfare purposes. However, it is known that the EOG signal loses some of the eye movement information when it travels through the skin, resulting in errors in EOG gaze estimation. Thus, EOG gaze estimation is difficult to point out the object accurately, and the object may not be appropriately grasped. Therefore, developing a methodology to compensate, for the lost information and increase spatial accuracy is important. This paper aims to realize highly accurate object grasping with a robot arm by combining EMG gaze estimation and the object recognition of camera image processing. The system consists of a robot arm, top and side cameras, a display showing the camera images, and an EOG measurement analyzer. The user manipulates the robot arm through the camera images, which can be switched, and the EOG gaze estimation can specify the object. In the beginning, the user gazes at the screen’s center position and then moves their eyes to gaze at the object to be grasped. After that, the proposed system recognizes the object in the camera image via image processing and grasps it using the object centroid. The object selection is based on the object centroid closest to the estimated gaze position within a certain distance (threshold), thus enabling highly accurate object grasping. The observed size of the object on the screen can differ depending on the camera installation and the screen display state. Therefore, it is crucial to set the distance threshold from the object centroid for object selection. The first experiment is conducted to clarify the distance error of the EOG gaze estimation in the proposed system configuration. As a result, it is confirmed that the range of the distance error is 1.8–3.0 cm. The second experiment is conducted to evaluate the performance of the object grasping by setting two thresholds from the first experimental results: the medium distance error value of 2 cm and the maximum distance error value of 3 cm. As a result, it is found that the grasping speed of the 3 cm threshold is 27% faster than that of the 2 cm threshold due to more stable object selection.

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Section: Introductionmentioning

confidence: 99%

Object Grasp Control of a 3D Robot Arm by Combining EOG Gaze Estimation and Camera-Based Object Recognition

et al. 2023

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“…Utilization of EOG signals is one method that has been widely developed in establishing communication between humans and machines [12], [14]. The development of EOG research has been carried out with various implementations, such as virtual keyboards as communication aids [8], [14]- [17], robot manipulator control [4], wheelchair control [18], [19], robot control [1], [20], mouse control [5], game development [2], IoT Application development [21], internet browsing application development [22], alarm system [23] and other developments.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Interface Efficiency: Adaptive Virtual Keyboard Minimizing Keystrokes in Electrooculography-Based Control

Anandika,

Laksono,

Suhaimi

et al. 2023

JNTE

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Rapid technological developments, one of which is technology to build communication relationships between humans and machines using Biosignals. One of them is Electrooculography (EOG). EOG is a type of biosignals obtained from eye movement. Research related to EOG has also developed a lot, especially for virtual keyboard control. Research on virtual keyboard control based on eye gaze motion using electrooculography technology has been widely developed. Previous research mostly drew conclusions based on time consumption in typing paragraphs. However, it has not been seen based on the number of eye gaze motions made by the user. In this research, an adaptive virtual keyboard system is built, controlled using EOG signals. The adaptive virtual keyboard is designed with 7x7 dimensions and has 49 buttons, including main buttons, letters, numbers, symbols, and unused buttons. The layout of the adaptive virtual keyboard has six zones. Each zone has a different number of steps. Characters located in the same zone have the same number of steps. The adaptive feature is to rearrange the position of the character's button based on the previously used characters. In the experiments, 30 respondents controlled static and adaptive virtual keyboards with 7 paragraphs typed. Adaptive mode rearranges the position of buttons based on k-selection activities from respondents. the k numbers are 10, 30, 50, 70 and 100. Two virtual keyboard modes are evaluated based on the number of steps required to type the paragraphs. Test results show that the performance of the adaptive virtual keyboard can shorten the number of user steps compared to static mode. There are tests of the optimal system that can be reduced up to 283 number of steps and from respondents, that can reduced up to 258 number of steps or about 40% of steps. This research underscores the promise of EOG-driven adaptive virtual keyboards, signaling a notable stride in augmenting user interaction efficiency in typing experiences, heralding a promising direction for future human-machine interface advancements.

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Electrooculography signal as alternative method to operate wheelchair based on SVM classifier

Rusydi,

Baiqi,

Rahman

et al. 2024

3RD CONFERENCE ON INNOVATION IN TECHNOLOGY AND ENGINEERING SCIENCE 2022 (CITES2022): Innovation in Technology and Science for N

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Controlling 3-D Movement of Robot Manipulator using Electrooculography

Cited by 8 publications

References 23 publications

Object Grasp Control of a 3D Robot Arm by Combining EOG Gaze Estimation and Camera-Based Object Recognition

Object Grasp Control of a 3D Robot Arm by Combining EOG Gaze Estimation and Camera-Based Object Recognition

Enhancing Interface Efficiency: Adaptive Virtual Keyboard Minimizing Keystrokes in Electrooculography-Based Control

Electrooculography signal as alternative method to operate wheelchair based on SVM classifier

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