Combining a Brain–Machine Interface and an Electrooculography Interface to perform pick and place tasks with a robotic arm

Hortal, Enrique; Íáñez, Eduardo; Úbeda, Andrés; Pérez-Vidal, Carlos; Azorín, José M.

doi:10.1016/j.robot.2015.05.010

Cited by 33 publications

(18 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent use of EOG (Belkacem et al 2015) shows 90% accuracy of eye movements using both EOG and electroencephalograms for computer interfacing and 80% accuracy for manipulation of a robotic arm (Hortal et al 2015). Nevertheless, the critical issue comes from the use of gels and sticky tapes, which cause skin irritation and allergic reactions.…”

Section: Introductionmentioning

confidence: 99%

Soft, conformal bioelectronics for a wireless human-wheelchair interface

Mishra

Norton

Yong-Kuk

et al. 2017

Biosensors and Bioelectronics

View full text Add to dashboard Cite

There are more than 3 million people in the world whose mobility relies on wheelchairs. Recent advancement on engineering technology enables more intuitive, easy-to-use rehabilitation systems. A human-machine interface that uses non-invasive, electrophysiological signals can allow a systematic interaction between human and devices; for example, eye movement-based wheelchair control. However, the existing machine-interface platforms are obtrusive, uncomfortable, and often cause skin irritations as they require a metal electrode affixed to the skin with a gel and acrylic pad. Here, we introduce a bioelectronic system that makes dry, conformal contact to the skin. The mechanically comfortable sensor records high-fidelity electrooculograms, comparable to the conventional gel electrode. Quantitative signal analysis and infrared thermographs show the advantages of the soft biosensor for an ergonomic human-machine interface. A classification algorithm with an optimized set of features shows the accuracy of 94% with five eye movements. A Bluetooth-enabled system incorporating the soft bioelectronics demonstrates a precise, hands-free control of a robotic wheelchair via electrooculograms.

show abstract

Section: Introductionmentioning

confidence: 99%

Soft, conformal bioelectronics for a wireless human-wheelchair interface

Mishra

Norton

Yong-Kuk

et al. 2017

Biosensors and Bioelectronics

View full text Add to dashboard Cite

show abstract

“…Other communication devices by using eye movement [6][7][8][9][10][11] had achieved the intricate communication as compared with the proposed system. Those communication devices require the capability of users' motion for more than a certain degree.…”

Section: Significance Of Proposed Systemmentioning

confidence: 99%

“…Development of communication aid devices improves the quality of daily life for such people. In the past, considerable numbers of devices for the purpose of communication aid and/or motor assistance have been developed for people with various movement disorders [3][4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Devices driven by residual muscles are effective for people with mild/moderate movement disorders [3][4][5]. Eye movement is one of useful motion as communication way by using residual muscles [6][7][8][9][10][11]. Electrooculography is also a measurement techniques of eye movement [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…Eye movement is one of useful motion as communication way by using residual muscles [6][7][8][9][10][11]. Electrooculography is also a measurement techniques of eye movement [6,7]. As one of techniques for extracting human motion features, image processing has been used in the communication aid system [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of a Non-Contact Nurse Call System by Image Processing of Eye Movement

Sugi

Minoda

Matsuda

et al. 2019

Transactions of ISCIE

View full text Add to dashboard Cite

This paper proposes a nurse call system driven by image processing of user's eye movements. The system consisting of a computer, a camera, two light emitting diode (LED) lamps and a speaker is capable of non-contact/non-constraint operation. A user moves his/her eyes according to the predetermined specific pattern, then, the system extracts those pattern of eye movements from the image. After detecting those pattern, the system makes a buzzing sound as same as the role of call button at bedside in hospital. During the system execution, a user can always grasp a current situation of the system operation as flashing pattern of two LED lamps. Construction and evaluation of the algorithm for detecting eye movement pattern from the image were done by using the data recorded from normal healthy subjects. Developed prototype system was used in a subject with movement disorders caused from nerve degeneration. Operation of the system by the subject was accomplished to be in a satisfactory level. Further improvement of the system was expected in the contribution to the communication aid for patients with various movement disorders.

show abstract