Feature selection and activity recognition from wearable sensors

Susanna Pirttikangas*, Kaori Fujinami, Tatsuo Nakajima

*Corresponding author for this work

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

194 Citations (Scopus)


We describe our data collection and results on activity recognition with wearable, coin-sized sensor devices. The devices were attached to four different parts of the body: right thigh and wrist, left wrist and to a necklace on 13 different testees. In this experiment, data was from 17 daily life examples from male and female subjects. Features were calculated from triaxial accelerometer and heart rate data within different sized time windows. The best features were selected with forward-back ward sequential search algorithm. Interestingly, acceleration mean values from the necklace were selected as important features. Two classifiers (multilayer perceptrons and kNN classifiers) were tested for activity recognition, and the best result (90.61 % aggregate recognition rate for 4-fold cross validation) was achieved with a kNN classifier.

Original languageEnglish
Title of host publicationUbiquitous Computing Systems - Third International Symposium, UCS 2006, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Print)3540462872, 9783540462873
Publication statusPublished - 2006
Event3rd International Symposium on Ubiquitous Computing Systems, UCS 2006 - Seoul, Korea, Republic of
Duration: 2006 Oct 112006 Oct 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4239 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Symposium on Ubiquitous Computing Systems, UCS 2006
Country/TerritoryKorea, Republic of

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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