HandSense: Capacitive coupling-based dynamic, micro finger gesture recognition

Viet Nguyen, Siddharth Rupavatharam, Luyang Liu, Richard Howard, Marco Gruteser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Head-mounted devices (HMD) for Augmented Reality (AR) are gaining traction thanks to a growing number of applications in the areas of image guided therapy, computer aided design, cargo packing, manufacturing and digital field service. However, providing an always available, intuitive and user friendly input for these devices remains a challenging problem. This paper explores recognizing dynamic, micro finger gestures using capacitive coupling for interacting with a head-mounted device. Electrodes are attached to fingertips of users gloves and capacitive coupling among all pairs of electrodes is measured quickly to infer the real-time spatial relationship between fingers. The system is able to recognize fine, low-effort finger gestures, such as swiping, sliding, tap, double-tap. We evaluated our prototype with 14 gestures executed by 10 subjects and found a 97% accuracy of gesture recognition.

Original languageEnglish (US)
Title of host publicationSenSys 2019 - Proceedings of the 17th Conference on Embedded Networked Sensor Systems
EditorsMi Zhang
PublisherAssociation for Computing Machinery, Inc
Pages285-297
Number of pages13
ISBN (Electronic)9781450369503
DOIs
StatePublished - Nov 10 2019
Event17th ACM Conference on Embedded Networked Sensor Systems, SenSys 2019 - New York, United States
Duration: Nov 10 2019Nov 13 2019

Publication series

NameSenSys 2019 - Proceedings of the 17th Conference on Embedded Networked Sensor Systems

Conference

Conference17th ACM Conference on Embedded Networked Sensor Systems, SenSys 2019
Country/TerritoryUnited States
CityNew York
Period11/10/1911/13/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Capacitive sensing
  • Gesture recognition
  • Human computer interaction (HCI)

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