TY - GEN
T1 - BARTON
T2 - 23rd IEEE International Conference on Parallel and Distributed Systems, ICPADS 2017
AU - Maag, Balz
AU - Zhou, Zimu
AU - Saukh, Olga
AU - Thiele, Lothar
N1 - 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].
PY - 2018/5/29
Y1 - 2018/5/29
N2 - 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.
AB - 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.
KW - Human computer interaction
KW - Pressure sensors
KW - Ubiquitous computing
UR - https://www.scopus.com/pages/publications/85048369117
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85048369117&origin=recordpage
U2 - 10.1109/ICPADS.2017.00013
DO - 10.1109/ICPADS.2017.00013
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781538621295
VL - 2017-December
T3 - Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS
SP - 9
EP - 16
BT - Proceedings - 2017 IEEE 23rd International Conference on Parallel and Distributed Systems, ICPADS 2017
PB - IEEE Computer Society
Y2 - 15 December 2017 through 17 December 2017
ER -