Designing a low-cost eyeball tracking keyboard for paralyzed people

AlKassim, Zeenat; Memon, Qurban A.

doi:10.1016/j.compeleceng.2017.01.007

Cited by 18 publications

(5 citation statements)

References 5 publications

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“…But the multi-modal stress detection system has not been applied as physiological parameters. The eye detection and tracking system was also suggested using classification techniques, and developed and tested to work with 90% double detection accuracy [18]. This system has not been tested on highresolution-based images as different types of head motion or colored background-based images.…”

Section: Literature Reviewmentioning

confidence: 99%

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

Chakraborty¹,

Ahmed²,

Yousuf³

et al. 2021

IEEE Access

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Attention is the mental awareness of human on a particular object or a piece of information. The level of attention indicates how intense the focus is on an object or an instance. In this study, several types of human attention level have been observed. After introducing image segmentation and detection technique for facial features, eyeball movement and gaze estimation were measured. Eye movement were assessed using the video data, and a total of 10197 data instances were manually labelled for the attention level. Then Artificial Neural Network (ANN) and Recurrent Neural Network-Long Short Term Memory (LSTM) based Deep learning (DL) architectures have been proposed for analysing the data. Next, the trained DL model has been implanted into a robotic system that is capable of detecting various features; ultimately leading to the calculation of visual attention for reading, browsing, and writing purposes. This system is capable of checking the attention level of the participants and also can detect if participants are present or not. Based on a certain level of visual focus of attention (VFOA), this system interacts with the person, generates awareness and establishes verbal or visual communication with that person. The proposed ML techniques have achieved almost 99.24% validation accuracy and 99.43% test accuracy. It is also shown in the comparative study that, since the dataset volumes are limited, ANN is more suitable for attention level calculation than RNN-LSTM. We hope that the implemented robotic structure manifests the real-world implication of the proposed method.

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Section: Literature Reviewmentioning

confidence: 99%

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

Chakraborty¹,

Ahmed²,

Yousuf³

et al. 2021

IEEE Access

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“…Researchers have already proposed many concepts on how to effectively coordinate and eliminate interference in large-scale MIMO two-layer networks in real-life scenarios, such as self-configuration, self-optimization, and selfhealing in ultra-dense deployment scenarios to achieve intelligent wireless network operations and reduce costs, which are all key technical points that need to be urgently addressed by researchers [8]. Also, researchers have proposed much corresponding interference coordination and elimination schemes, such as virtual layer technology (single-layer solid network to build virtual multilayer network) and cell dynamic clustering (base stations in the network cluster, the same cluster of base stations using the same frequency band resources), but the actual use of the effect is still to be further tested.…”

Section: Related Workmentioning

confidence: 99%

MIMO Cross-Layer Secure Communication Algorithm for Cyber Physical Systems Based on Interference Strategies

Qiu

2020

IEEE Access

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“…HaarCascade method can be applied to detect certain features of the face and Integral Projection can be used to obtain the positions of the movements of the eye stated by authors [2]. The scanning keyboard is expandable, as additional buttons can be included as suggested by enat Al-Kassim [1] et al They further claim that false detection can be minimized by using high resolution images, allowing colored backgrounds as well as allowing further head orientations. Menu selection with numbers and shortcuts increases the system's possibilities [3].Gaze isn't as precise as the mouse.…”

Section: Related Workmentioning

confidence: 99%

HCI Based Input Device for Differently Abled

Brammi¹,

Dhivya²,

Padmavathy³

et al. 2020

Intelligent Systems and Computer Technology

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Tremendous advancements in the technology has lead to the monumental growth of various branches of computing such as Computer Vision and Human-Computer Interaction (HCI). Computer’s input has data about different properties of users, objects or places. For instance, mouse and keyboard works by the movement performed by the end user’s hands. These approaches are not appropriate for differently abled people. Away to create an application which replaces the input devices such as mouse and keyboard by using face of the user is proposed. This paper introduces how head motion of the user can be used to control the mouse cursor and how gaze tracking can be used to control the keyboard. A face detecting system precisely records the motion parameters from video at real-time using a typical webcam. While the pace reduces while using the virtual mouse and keyboard, the performance of the system is intact for differently abled people whose only means of communication are the head movements and gaze.

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Designing a low-cost eyeball tracking keyboard for paralyzed people

Cited by 18 publications

References 5 publications

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

MIMO Cross-Layer Secure Communication Algorithm for Cyber Physical Systems Based on Interference Strategies

HCI Based Input Device for Differently Abled

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