BARTON : Low power tongue movement sensing with in-ear barometers

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

11 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 23rd International Conference on Parallel and Distributed Systems, ICPADS 2017
PublisherIEEE Computer Society
Pages9-16
Volume2017-December
ISBN (print)9781538621295
Publication statusPublished - 29 May 2018
Externally publishedYes

Publication series

NameProceedings of the International Conference on Parallel and Distributed Systems - ICPADS
Volume2017-December
ISSN (Print)1521-9097

Conference

Title23rd IEEE International Conference on Parallel and Distributed Systems, ICPADS 2017
PlaceChina
CityShenzhen
Period15 - 17 December 2017

Abstract

Sensing tongue movements enables various applications in hands-free interaction and alternative communication. We propose BARTON, a BARometer based low-power and robust TONgue movement sensing system. Using a low sampling rate of below 50 Hz, and only extracting simple temporal features from in-ear pressure signals, we demonstrate that it is plausible to distinguish important tongue gestures (left, right, forward) at low power consumption. We prototype BARTON with commodity earpieces integrated with COTS barometers for in-ear pressure sensing and an ARM micro-controller for signal processing. Evaluations show that BARTON yields 94% classification accuracy and 8.4 mW power consumption, which achieves comparable accuracy, but consumes 44 times lower energy than the state-of-the-art microphone-based solutions. BARTON is also robust to head movements and operates with music played directly from earphones. © 2017 IEEE.

Research Area(s)

  • Human computer interaction, Pressure sensors, Ubiquitous computing

Bibliographic Note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Citation Format(s)

BARTON: Low power tongue movement sensing with in-ear barometers. / Maag, Balz; Zhou, Zimu; Saukh, Olga et al.
Proceedings - 2017 IEEE 23rd International Conference on Parallel and Distributed Systems, ICPADS 2017. Vol. 2017-December IEEE Computer Society, 2018. p. 9-16 (Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS; Vol. 2017-December).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review