System I4Control®: Contactless control PC

Fejtová, Marcela; Fejt, Jan; Novák, Petr; Štěpánková, Olga

doi:10.1109/ines.2006.1689387

Cited by 4 publications

(5 citation statements)

References 1 publication

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“…From the existing works, gaze gestures (GG) are widely captured with either one of these three groups of input sensors as depicted in Table 1, depending on the application, availability, and cost. The first group addressed motion-based sensors for GG including EOG which works based on an electric potential difference between the retina and cornea causing changes in the electrostatic field [8]. These position changes were sensed by the electrodes attached to the users' skin closer to the eyes as in [9], [10], [11], and [12].…”

Section: Related Workmentioning

confidence: 99%

“…The third group is Video oculography (VOG) which is the most adopted nowadays because, it can capture the images of the subject eyes, estimate eyes positions, and point of gaze (POG) i.e where the user is looking [17], [18]. The first sub-group of VOG is camera-based, which detects pupil pose and converts them into coordinates [8], [2], [19], [20]. But camera-based suffers from interpolation, head movement, segmentation, identification, and is sometimes cumbersome.…”

Section: Related Workmentioning

confidence: 99%

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Boosted Gaze Gesture Recognition Using Underlying Head Orientation Sequence

et al. 2023

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People find it challenging to control smart systems with complex gaze gestures due to the vulnerability of eye saccades. Instead, the existing works achieved good recognition accuracy of simple gaze gestures because of sufficient eye gaze points but simple gaze gestures have limited applications compared to complex gaze gestures. Complex gaze gestures need a composition of multiple subunits of eye fixation to contain a sequence of gaze points that are clustered and rotated with an underlying complex head orientation relationship. This paper proposes a new set of eye gaze points and head orientation angles as new sequences to recognize complex gaze gestures. Eye gaze points and head orientation angles have a powerful influence on gaze gesture formation. The new sequence was obtained by aligning clustered gaze points and head orientation angles with a simple moving average (SMA) to denoise and interpolate the gap between successive eye fixations. The aligned new sequence of complex gaze gestures was utilized to train sequential machine learning (ML) algorithms. To evaluate the performance of the proposed method, we recruited and recorded the eye gaze and head orientation features of ten participants using an eye tracker. The results show that Boosted Hidden Markov Models (HMM) using Random Subspace methods achieved the best accuracies of 94.72% and 98.1% for complex, and simple gestures respectively, which outperformed the conventional methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Boosted Gaze Gesture Recognition Using Underlying Head Orientation Sequence

et al. 2023

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“…People with physical impairments and disabilities rely on gaze-assisted interaction to communicate and perform basic operations on a computer [3]. Also, in the scenarios of situationally-induced impairments and disabilities, gaze-assisted interaction is crucial [27], [28]. While dwell-based selection has been majorly used in gaze-assisted interactions, using dwell has various limitations related to accuracy, performance, and usability [7], [29], [30].…”

Section: Related Workmentioning

confidence: 99%

“…The authors found that visual feedback significantly reduced the number of selection errors. Fejtová et al [28] created a system called ''I4Control'' which allows individuals with special needs to make non-contact control of a personal computer through the eye or head movement.…”

Section: B Accessible Interactionsmentioning

confidence: 99%

Evaluating the Performance of Machine Learning Algorithms in Gaze Gesture Recognition Systems

Ray

Rajanna

et al. 2022

IEEE Access

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Despite the utility of gaze gestures as an input method, there is a lack of guidelines available regarding how to design gaze gestures, what algorithms to use for gaze gesture recognition, and how these algorithms compare in terms of performance. To facilitate the development of applications that leverage gaze gestures, we have evaluated the performance of a combination of template-based and data-driven algorithms on two custom gesture sets that can map to user actions. Template-based algorithms had consistently high accuracies but the slowest runtimes, making them best for small gesture sets or accuracy-critical applications. Data-driven algorithms run much faster and scale better to larger gesture sets, but require more training data to achieve the accuracy of the template-based methods. The main takeaways for gesture set design are 1) gestures should have distinct forms even when performed imprecisely and 2) gestures should have clear key-points for the eyes to fixate onto.INDEX TERMS Eye tracking, gaze gestures, gesture design, human-computer interaction, multimodal interaction.

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“…Various techniques were developed that enabled persons with motor-impairment to control the computer via emulation of the keyboard and the mouse, such as one-switch input [2], voice input [3], or eye tracking [1]. These methods are however tailored to the most common applications and interactions.…”

Section: Introductionmentioning

confidence: 99%

Toward accessible technology for music composers and producers with motor disabilities

Sporka

Carson

Nauert

et al. 2013

Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility

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In an initial user study, three motor-impaired musicians-a composer with a degenerative motor neuron disease, a guitarist who suffered a stroke, and a first-year college student with impaired finger movement-identified prospective areas of research in assistive technology. Participants in the study made use of a range of technologies to adapt conventional software to their needs, and identified practical limitations and challenges in those adaptations, including suggestions for novel and intuitive interfaces, optimized control-surface layouts, and repurposing opportunities in text-input techniques.

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System I4Control®: Contactless control PC

Cited by 4 publications

References 1 publication

Boosted Gaze Gesture Recognition Using Underlying Head Orientation Sequence

Boosted Gaze Gesture Recognition Using Underlying Head Orientation Sequence

Evaluating the Performance of Machine Learning Algorithms in Gaze Gesture Recognition Systems

Toward accessible technology for music composers and producers with motor disabilities

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