Recognizing upper body postures using textile strain sensors

C. Mattmann, O.D. Amft, H. Harms, G. Tröster, F. Clemens

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

127 Citations (Scopus)
319 Downloads (Pure)

Abstract

In this paper we present a garment prototype using strain sensors to recognize upper body postures. A novel thermoplastic elastomer strain sensor was used for measuring strain in the clothing. This sensor has a linear resistance response to strain, a small hysteresis and can be fully integrated into textile. A study was conducted with eight participants wearing the garment and performing a total of 27 upper body postures. A Naïve Bayes classification was applied to identify the different postures. Nearly a complete recognition rate of 97% was achieved when the classification was adapted to the individual participant. A classification rate of 84% was achieved for an all-user classification and 65% for an independent user. These results show the feasibility to recognize postures with our setup, even in an unseen user setting. Furthermore, we used the garment prototype in a gym experiment to explore its potential for rehabilitation and fitness training. Intensity, speed and number of repetitions could be obtained from the garment sensor data. © 2007 IEEE.
Original languageEnglish
Title of host publicationProceedings 11th IEEE International Symposium on Wearable Computers, ISWC 2007, 11 October 2007 through 13 October 2007, Boston, MA
Pages29-36
DOIs
Publication statusPublished - 2007

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