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FinD: Detection of Finger Movement using Smart Watch

Published: 20 September 2018 Publication History

Abstract

There were several trials to detect the movement of fingers, they necessitated special sensors, either rings or gloves, attached to the fingers. Based on the background, we have developed a system to identify the moved finger called FinD using the acceleration signals obtained with Commercial Off-The-Shelf (COTS) smart watches. We have investigated two methods: Non Matrix Factorization (NMF)-based (FinD-NMF) and time-series-based (FinD-TS) analyses. We have compared the two methods for three fingers with three subjects and found that FinD-TS exceeds FinD-NMF in the accuracy up to 40%-80%.

References

[1]
Masa Ogata, Yuta Sugiura, Hirotaka Osawa, and Michita Imai, 2012. "iRing: Intelligent Ring Using Infrared Reflection", Proceedings of the 25th annual ACM symposium on User interface software and technology (UIST '12), pp. 131--136
[2]
Christoph Amma, Marcus Georgi, and Tanja Schultz, 2012. "Airwriting: Hands-Free Mobile Text Input by Spotting. and Continuous Recognition of 3d-Space Handwriting with Inertial Sensors", Proceedings of the 2012 16th Annual International Symposium on Wearable Computers (ISWC '12), pp. 52--29
[3]
Chao Xu, Parth H. Pathak, and Prasant Mohapatra, 2015. "Finger-writing with Smartwatch: A Case for Finger and Hand Gesture Recognition using Smartwatch", Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications (HotMobile '15), pp. 9--14
[4]
Hongyi Wen, Julian Ramos Rojas, and Anind K. Dey, 2016. "Serendipity: Finger gesture recognition using an off-the-shelf smartwatch." Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM.

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  1. FinD: Detection of Finger Movement using Smart Watch

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    cover image ACM Other conferences
    iWOAR '18: Proceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction
    September 2018
    148 pages
    ISBN:9781450364874
    DOI:10.1145/3266157
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    In-Cooperation

    • Fraunhofer IGD: Fraunhofer Institute for Computer Graphics Research IGD
    • Rostock: University of Rostock

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 September 2018

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    Author Tags

    1. Gesture
    2. Non-Matrix Factorization
    3. Smart Watch
    4. machine learning

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    iWOAR '18

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    iWOAR '18 Paper Acceptance Rate 15 of 28 submissions, 54%;
    Overall Acceptance Rate 46 of 73 submissions, 63%

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