Optically sensing tongue gestures for computer input

Saponas, T. Scott; Kelly, Damien P.; Parviz, Babak A.; Tan, Desney

doi:10.1145/1622176.1622209

Cited by 77 publications

(35 citation statements)

References 5 publications

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“…More unconventional solutions to the mobile input problem include sensors placed on the tongue [11], behind ears [8], or on cheeks [12]. All these approaches require the user to wear specialized sensor electronics and might not be feasible in real-world scenarios.…”

Section: Related Workmentioning

confidence: 99%

Joint Estimation of 3D Hand Position and Gestures from Monocular Video for Mobile Interaction

Song

Pece

Sörös

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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We present a machine learning technique to recognize gestures and estimate metric depth of hands for 3D interaction, relying only on monocular RGB video input. We aim to enable spatial interaction with small, body-worn devices where rich 3D input is desired but the usage of conventional depth sensors is prohibitive due to their power consumption and size. We propose a hybrid classification-regression approach to learn and predict a mapping of RGB colors to absolute, metric depth in real time. We also classify distinct hand gestures, allowing for a variety of 3D interactions. We demonstrate our technique with three mobile interaction scenarios and evaluate the method quantitatively and qualitatively.

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Section: Related Workmentioning

confidence: 99%

Joint Estimation of 3D Hand Position and Gestures from Monocular Video for Mobile Interaction

Song

Pece

Sörös

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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“…Only a few studies have explored the use of specific face areas, such as the ear [16] or the tongue [25] for input. We explore the range of different face areas, focusing on the design and social suitability of hand-to-face input, as well as its impact on effective document navigation.…”

Section: On-body Interactionmentioning

confidence: 99%

Exploring the use of hand-to-face input for interacting with head-worn displays

Serrano

Ens

Irani

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

101

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We propose the use of Hand-to-Face input, a method to interact with head-worn displays (HWDs) that involves contact with the face. We explore Hand-to-Face interaction to find suitable techniques for common mobile tasks. We evaluate this form of interaction with document navigation tasks and examine its social acceptability. In a first study, users identify the cheek and forehead as predominant areas for interaction and agree on gestures for tasks involving continuous input, such as document navigation. These results guide the design of several Hand-to-Face navigation techniques and reveal that gestures performed on the cheek are more efficient and less tiring than interactions directly on the HWD. Initial results on the social acceptability of Hand-to-Face input allow us to further refine our design choices, and reveal unforeseen results: some gestures are considered culturally inappropriate and gender plays a role in selection of specific Hand-to-Face interactions. From our overall results, we provide a set of guidelines for developing effective Hand-to-Face interaction techniques.

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“…Sensing body activity directly presents an alternative to using the body as a spatial input canvas, such as sensing muscle tension [37], tongue motions using prototypes worn inside the mouth [21,36] and micro-devices integrated into worn contact lenses [19]. Direct output through the body has been shown with electrodes that stimulate the user's muscles [44] or the user's ear to influence the sense of balance [9].…”

Section: Input and Output With The Bodymentioning

confidence: 99%

Implanted user interfaces

Holz

Grossman

Fitzmaurice

et al. 2012

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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We investigate implanted user interfaces that small devices provide when implanted underneath human skin. Such devices always stay with the user, making their implanted user interfaces available at all times. We discuss four core challenges of implanted user interfaces: how to sense input through the skin, how to produce output, how to communicate amongst one another and with external infrastructure, and how to remain powered. We investigate these four challenges in a technical evaluation where we surgically implant study devices into a specimen arm. We find that traditional interfaces do work through skin. We then demonstrate how to deploy a prototype device on participants, using artificial skin to simulate implantation. We close with a discussion of medical considerations of implanted user interfaces, risks and limitations, and project into the future.

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Optically sensing tongue gestures for computer input

Cited by 77 publications

References 5 publications

Joint Estimation of 3D Hand Position and Gestures from Monocular Video for Mobile Interaction

Joint Estimation of 3D Hand Position and Gestures from Monocular Video for Mobile Interaction

Exploring the use of hand-to-face input for interacting with head-worn displays

Implanted user interfaces

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