Hands free interface for Human Computer Interaction

Chathuranga, S. K.; Samarawickrama, K. C.; Chandima, H. M. L.; Chathuranga, K. G. T. D.; Abeykoon, A. M. Harsha S.

doi:10.1109/iciafs.2010.5715687

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…In addition to the techniques using gaze and voice, numerous interfaces using face gesture [19], nose [20], head direction [21], foot [22], and EMG [23], [24] have been developed. However, these techniques are difficult to employ in various applications due to the limited environment and usability limitations.…”

Section: A Input Technologies As a Hands-free Interfacementioning

confidence: 99%

The Study of a Classification Technique for Numeric Gaze-Writing Entry in Hands-Free Interface

2019

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Recently, many applications are developed in numerous domains with various environments. Since some environments require hands-free applications, new technology is needed for the input interfaces other than the mouse and keyboard. Therefore, to meet the needs, many researchers have begun to investigate the gaze and voice for the input technology. In particular, there are many approaches to render virtual keyboards with the gaze. However, since the virtual keyboards hide the screen space, this technique can only be applied in limited environments. In this paper, we propose a classification technique for gaze-written numbers as the hands-free interface. Since the gaze-writing is less accurate compared to the virtual keyboard typing, we apply the convolutional neural network (CNN) deep learning algorithm to recognize the gaze-writing and improve the classification accuracy. Besides, we create new gaze-writing datasets for training, gaze MNIST (gMNIST), by modifying the MNIST data with features of the gaze movement patterns. For the evaluation, we compare our approach with the basic CNN structures using the original MNIST dataset. Our study will allow us to have more options for the input interfaces and expand our choices in hands-free environments. INDEX TERMS Gaze-writing, input technique, MNIST, Eye tracking, machine learning.

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Section: A Input Technologies As a Hands-free Interfacementioning

confidence: 99%

The Study of a Classification Technique for Numeric Gaze-Writing Entry in Hands-Free Interface

2019

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“…The ability to control commands is an important consideration. There have been many approaches to realize selection, such as fixing the cursor position for an interval [9]; eye movement, such as blinking [7], [10] or winking [11]; and speech recognition [12]. The application of facial feature tracking to mobile devices is an active research area.…”

Section: B Facial Feature Detectionmentioning

confidence: 99%

Combined Use of Rear Touch Gestures and Facial Feature Detection to Achieve Single-Handed Navigation of Mobile Devices

Onuki

Kumazawa

2016

IEEE Trans. Human-Mach. Syst.

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To improve single-handed operation of mobile devices, rear touch panel operation to control commands and facial feature detection to control cursor position are proposed. Operational control is achieved through finger chord gestures on a rear touch panel, and nose movement is used to control cursor movement. Zooming is achieved by detecting the apparent distance between the left and right eyes in conjunction with a finger chord gesture. We have evaluated movement time, error rates, and the throughputs of these techniques in comparison with the conventional single-handed front touch panel thumb operations using Fitts's law. Experiments have been conducted to evaluate two operation modes, selection and zooming, in the form of reciprocal 1-D pointing tasks with 12 participants. For the target selection task, the proposed technique achieved 12% (0.26 s) shorter movement time and 4.7% smaller error rate than the conventional method on average. Especially, for long distance targets, the performance of the conventional method became remarkably inferior due to the limit of reach of the thumb, whereas the proposed technique achieved much less deterioration and obtained expected performance because the cursor could reach anywhere on the display. For the target size adjustment task, the proposed technique achieved 9% (0.22 s) shorter movement time than the conventional method and obtained a comparable error rate of less than 4%. Consequently, we could demonstrate the techniques that make single-handed select and zoom operations available anywhere on a large-sized tablet device with no blockage of the display.

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“…Por outro lado, para facilitar a interação de computadores para pessoas com deficiência (Chathuranga, Samarawickrama, Chandima, Chathuranga, & Abeykoon, 2010) presenteiam-nos uma solução abrangente para o problema onde, como alternativa para o rato, são utilizadas técnicas de VC e tecnologias de reconhecimento de voz. Através dos movimentos do nariz, o utilizador consegue mover o ponteiro do rato e através de comandos por voz pode fazer as outras ações relativas ao manuseamento do rato.…”

Section: Interfaces Baseadas No Reconhecimento Da Vozunclassified

Natural Interactions: An Application for Gestural Hands Recognition

Proença

Guerra

2013

IFIP Advances in Information and Communication Technology

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Hands free interface for Human Computer Interaction

Cited by 12 publications

References 8 publications

The Study of a Classification Technique for Numeric Gaze-Writing Entry in Hands-Free Interface

The Study of a Classification Technique for Numeric Gaze-Writing Entry in Hands-Free Interface

Combined Use of Rear Touch Gestures and Facial Feature Detection to Achieve Single-Handed Navigation of Mobile Devices

Natural Interactions: An Application for Gestural Hands Recognition

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