Using kernels for a video-based mouse-replacement interface

Epstein, Samuel; Missimer, Eric; Betke, Margrit

doi:10.1007/s00779-012-0617-z

Cited by 15 publications

(13 citation statements)

References 27 publications

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“…Invasive BCIs range from 5.4–69 bits/min (Brunner, Ritaccio, Emrich, Bischof, & Schalk, 2011; Guenther & Brumberg, 2011; Hill et al, 2006; Simeral, Kim, Black, Donoghue, & Hochberg, 2011), whereas non-invasive BCIs can range from 1.8–24 bits/min (Nijboer et al, 2008; Sellers, Krusienski, McFarland, Vaughan, & Wolpaw, 2006; Wolpaw et al, 2002). Eye tracking can produce ITRs in the range of 60–222 bits/min (Frey et al, 1990; Higginbotham et al, 2007; Liu et al, 2012; Majaranta et al, 2006, MacKenzie, Aula, & Räihä, 2006), and ITRs produced with head tracking devices range from 78–174 bits/min (Epstein, Missimer, & Betke, 2014; Williams & Kirsch, 2008). Mechanical switches can produce ITRs in the range of 96–198 bits/min.…”

Section: Discussionmentioning

confidence: 99%

Surface electromyographic control of a novel phonemic interface for speech synthesis

Cler

Nieto-Castañón

Guenther

et al. 2016

Augmentative and Alternative Communication

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Many individuals with minimal movement capabilities use AAC to communicate. These individuals require both an interface with which to construct a message (e.g., a grid of letters) and an input modality with which to select targets. This study evaluated the interaction of two such systems: (a) an input modality using surface electromyography (sEMG) of spared facial musculature, and (b) an onscreen interface from which users select phonemic targets. These systems were evaluated in two experiments: (a) participants without motor impairments used the systems during a series of 8 training sessions, and (b) one individual who uses AAC used the systems for two sessions. Both the phonemic interface and the electromyographic cursor show promise for future AAC applications.

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Section: Discussionmentioning

confidence: 99%

Surface electromyographic control of a novel phonemic interface for speech synthesis

Cler

Nieto-Castañón

Guenther

et al. 2016

Augmentative and Alternative Communication

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“…However, the tracking method is also vulnerable to colour and illumination environment, and requires initialization step. In [19], the authors alleviated the "feature drift" and illuminationdependent problems of [15]. However, that system is not very robust yet, especially when the motion of the user is fast.…”

Section: ) Motion Tracking Based Atsmentioning

confidence: 96%

“…These ATs track motions of body parts, such as eye [13], [14] / tongue [1], [8] / head [15], [16] / face [17], [18], [19] trackers. In [13], [14], eye gaze was directly used to select the target.…”

Section: ) Motion Tracking Based Atsmentioning

confidence: 99%

Facial Position and Expression-Based Human–Computer Interface for Persons With Tetraplegia

Bian

Hou

Chau

et al. 2016

IEEE J. Biomed. Health Inform.

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A human-computer interface (namely Facial position and expression Mouse system, FM) for the persons with tetraplegia based on a monocular infrared depth camera is presented in this paper. The nose position along with the mouth status (close/open) is detected by the proposed algorithm to control and navigate the cursor as computer user input. The algorithm is based on an improved Randomized Decision Tree, which is capable of detecting the facial information efficiently and accurately. A more comfortable user experience is achieved by mapping the nose motion to the cursor motion via a nonlinear function. The infrared depth camera enables the system to be independent of illumination and color changes both from the background and on human face, which is a critical advantage over RGB camera-based options. Extensive experimental results show that the proposed system outperforms existing assistive technologies in terms of quantitative and qualitative assessments.

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“…We will test other mouse-replacement systems as well, for example, the beta version of the Camera Mouse, called "Camera Mouse Suite'' [4], which is available for free download on the internet and includes the Camera Mouse team's most recent work: (1) an optical-flow-based version of the Camera Mouse [1] (2) a version that uses kernels as the tracking mechanism [5], (3) an interface that interprets eyebrow raises for input of scanbased text-entry systems [9] and (4) an interface that provides full pointer control via blink and wink detection [11].…”

Section: Future Workmentioning

confidence: 99%

Preliminary investigation of the impact of visual feedback on a camera-based mouse-replacement system

Feng

Chen

Betke

2013

Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments

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This paper reports the design, implementation, and results of a carefully designed experiment that examined the performance of a camera-based mouse-replacement interface that was supported with visual feedback. Four different visual feedback modes were tested during the pointing-task experiment. Quantitative results, based on three metrics, do not show statistically significant difference between these modes. Qualitative feedback from the participants of the experiments, however, shows that user experience is improved by static and animated visual feedback during the pointing task.

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Using kernels for a video-based mouse-replacement interface

Cited by 15 publications

References 27 publications

Surface electromyographic control of a novel phonemic interface for speech synthesis

Surface electromyographic control of a novel phonemic interface for speech synthesis

Facial Position and Expression-Based Human–Computer Interface for Persons With Tetraplegia

Preliminary investigation of the impact of visual feedback on a camera-based mouse-replacement system

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