Geometric Compression of Orientation Signals for Fast Gesture Analysis

Aswin Sivakumar, Rushil Anirudh, Pavan Turaga

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

Abstract

This paper concerns itself with compression strategies for orientation signals, seen as signals evolving on the space of quaternion's. The compression techniques extend classical signal approximation strategies used in data mining, by explicitly taking into account the quotient-space properties of the quaternion space. The approximation techniques are applied to the case of human gesture recognition from cell phone-based orientation sensors. Results indicate that the proposed approach results in high recognition accuracies, with low storage requirements, with the geometric computations providing added robustness than classical vector-space computations.

Original languageEnglish (US)
Title of host publicationData Compression Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages423-432
Number of pages10
Volume2015-July
ISBN (Print)9781479984305
DOIs
StatePublished - Jul 2 2015
Event2015 Data Compression Conference, DCC 2015 - Snowbird, United States
Duration: Apr 7 2015Apr 9 2015

Other

Other2015 Data Compression Conference, DCC 2015
CountryUnited States
CitySnowbird
Period4/7/154/9/15

Keywords

  • gesture recognition
  • quaternion data
  • Riemannian manifolds
  • symbolic approximation

ASJC Scopus subject areas

  • Computer Networks and Communications

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