Eye Gaze Controlled Immersive Video Navigation System for Disabled People

Saha, Dipayan; Sayyed, A. Q. M. Sazzad; Saif, Ahmed; Shahnaz, Celia; Fattah, Shaikh Anowarul

doi:10.1109/r10-htc47129.2019.9042470

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…Thus, assistive technologies (ATs) seek to enhance the quality of life for individuals with severe physical disabilities by improving or substituting motor functions, consequently boosting their autonomy [5][6][7]. However, as ALS progresses to the late stages, most commercially available interfaces, such as joystick controllers [8,9], tonguebased systems [10,11], and eye-gaze-operated devices [12,13], become unusable. Brain-Computer Interfaces (BCIs) bypass the lack of motor function, as they translate brain activity directly into control commands for the assistive device [14].…”

Section: Introductionmentioning

confidence: 99%

Manual 3D Control of an Assistive Robotic Manipulator Using Alpha Rhythms and an Auditory Menu: A Proof-of-Concept

Cardoso

Kæseler

Jochumsen

et al. 2022

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Brain–Computer Interfaces (BCIs) have been regarded as potential tools for individuals with severe motor disabilities, such as those with amyotrophic lateral sclerosis, that render interfaces that rely on movement unusable. This study aims to develop a dependent BCI system for manual end-point control of a robotic arm. A proof-of-concept system was devised using parieto-occipital alpha wave modulation and a cyclic menu with auditory cues. Users choose a movement to be executed and asynchronously stop said action when necessary. Tolerance intervals allowed users to cancel or confirm actions. Eight able-bodied subjects used the system to perform a pick-and-place task. To investigate the potential learning effects, the experiment was conducted twice over the course of two consecutive days. Subjects obtained satisfactory completion rates (84.0 ± 15.0% and 74.4 ± 34.5% for the first and second day, respectively) and high path efficiency (88.9 ± 11.7% and 92.2 ± 9.6%). Subjects took on average 439.7 ± 203.3 s to complete each task, but the robot was only in motion 10% of the time. There was no significant difference in performance between both days. The developed control scheme provided users with intuitive control, but a considerable amount of time is spent waiting for the right target (auditory cue). Implementing other brain signals may increase its speed.

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