WaveTrace: motion matching input using Wrist-Worn Motion sensors

D. Verweij, A.E. Esteves, J.V. Khan, S. Bakker

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

8 Citations (Scopus)
2 Downloads (Pure)

Abstract

We present WaveTrace, a novel interaction technique based on selection by motion matching. In motion matching systems, targets move continuously in a singular and pre-defined path -- users interact with these by performing a synchronous bodily movement that matches the movement of one of the targets. Unlike previous work which tracks user input through optical systems, WaveTrace is arguably the first motion matching technique to rely on motion data from inertial measurement units readily available in many wrist-worn wearable devices such as smart watches. To evaluate the technique, we conducted a user study in which we varied: hand; degrees of visual angle; target speed; and number of concurrent targets. Preliminary results indicate that the technique supports up to eight concurrent targets; and that participants could select targets moving at speeds between 180 and 270/s (mean acquisition time of 2237ms, and average success rate of 91%).
Original languageEnglish
Title of host publicationCHI EA 17 : Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages2180-2186
ISBN (Print)978-1-4503-4656-6
DOIs
Publication statusPublished - 2017
Event35th Annual ACM CHI Conference on Human Factors in Computing Systems (CHI 2017) - Colorado Convention Center, Denver, United States
Duration: 6 May 201711 May 2017
Conference number: 35
https://chi2017.acm.org/
http://www.scopus.com/inward/record.url?scp=85019570374&partnerID=8YFLogxK (Link to publication in Scopus)

Conference

Conference35th Annual ACM CHI Conference on Human Factors in Computing Systems (CHI 2017)
Abbreviated titleACM CHI 2017
CountryUnited States
CityDenver
Period6/05/1711/05/17
Internet address

Fingerprint

Units of measurement
Watches
Sensors
Optical systems

Cite this

Verweij, D., Esteves, A. E., Khan, J. V., & Bakker, S. (2017). WaveTrace: motion matching input using Wrist-Worn Motion sensors. In CHI EA 17 : Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 2180-2186). New York: Association for Computing Machinery, Inc. https://doi.org/10.1145/3027063.3053161
Verweij, D. ; Esteves, A.E. ; Khan, J.V. ; Bakker, S. / WaveTrace: motion matching input using Wrist-Worn Motion sensors. CHI EA 17 : Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. New York : Association for Computing Machinery, Inc, 2017. pp. 2180-2186
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abstract = "We present WaveTrace, a novel interaction technique based on selection by motion matching. In motion matching systems, targets move continuously in a singular and pre-defined path -- users interact with these by performing a synchronous bodily movement that matches the movement of one of the targets. Unlike previous work which tracks user input through optical systems, WaveTrace is arguably the first motion matching technique to rely on motion data from inertial measurement units readily available in many wrist-worn wearable devices such as smart watches. To evaluate the technique, we conducted a user study in which we varied: hand; degrees of visual angle; target speed; and number of concurrent targets. Preliminary results indicate that the technique supports up to eight concurrent targets; and that participants could select targets moving at speeds between 180 and 270/s (mean acquisition time of 2237ms, and average success rate of 91{\%}).",
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Verweij, D, Esteves, AE, Khan, JV & Bakker, S 2017, WaveTrace: motion matching input using Wrist-Worn Motion sensors. in CHI EA 17 : Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. Association for Computing Machinery, Inc, New York, pp. 2180-2186, 35th Annual ACM CHI Conference on Human Factors in Computing Systems (CHI 2017), Denver, United States, 6/05/17. https://doi.org/10.1145/3027063.3053161

WaveTrace: motion matching input using Wrist-Worn Motion sensors. / Verweij, D.; Esteves, A.E.; Khan, J.V.; Bakker, S.

CHI EA 17 : Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. New York : Association for Computing Machinery, Inc, 2017. p. 2180-2186.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Verweij D, Esteves AE, Khan JV, Bakker S. WaveTrace: motion matching input using Wrist-Worn Motion sensors. In CHI EA 17 : Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. New York: Association for Computing Machinery, Inc. 2017. p. 2180-2186 https://doi.org/10.1145/3027063.3053161