Jerk-based Feature Extraction for Robust Activity Recognition from Acceleration Data

Wilhelmiina Hämäläinen, Mikko Järvinen, Paula Martiskainen, Jaakko Mononen

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

Abstract

A current trend in activity recognition is to use just one easily carried accelerometer, either integrated into a mobile phone, carried in a pocket, or attached to an animal's collar. The main disadvantage of this approach is that the orientation of the accelerometer is generally unknown. Therefore, one cannot separate body-related accelerations from the gravitational acceleration or determine the real directions of the observed accelerations accurately. As a solution, we introduce a new technique where jerk (changes of accelerations) is analyzed instead of the original acceleration signal. The total jerk magnitude is completely orientation-independent and it reflects only body-related accelerations. If the direction of the gravitation can be approximated even loosely, then the jerk signal can be further enriched with valuable information on jerk angles (direction changes). According to our experiments this kind of jerk-filtered signal produces robust features and can improve the recognition accuracy remarkably.
Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Intelligent Systems Design and Applications
PublisherIEEE Computer Society
Pages831-836
Publication statusPublished - 2011
MoE publication typeA4 Article in a conference publication

Keywords

  • Activity recognition
  • feature extraction
  • accelerometer
  • jerk filter

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