| Home | Projects | Publications | Events | Contact |
• Sorted by Date •
Corinne Mattmann, Oliver Amft, Holger Harms, Gerhard Tröster, and Frank Clemens. Recognizing Upper Body Postures using Textile Strain Sensors. In ISWC 2007: Proceedings of the 11th IEEE International Symposium on Wearable Computers, pp. 29–36, IEEE Press, October 2007. Recipient of the IEEE ISWC 2007 Best Paper Award.
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 Naive 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.
@INPROCEEDINGS{Mattmann2007-P_ISWC,
author = {Corinne Mattmann and Oliver Amft and Holger Harms and Gerhard Tr\"{o}ster
and Frank Clemens},
title = {Recognizing Upper Body Postures using Textile Strain Sensors},
booktitle = {ISWC 2007: Proceedings of the 11th IEEE International Symposium on
Wearable Computers},
year = {2007},
pages = {29--36},
month = {October},
publisher = {IEEE Press},
note = {Recipient of the IEEE ISWC 2007 Best Paper Award.},
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 Naive 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.},
doi = {10.1109/ISWC.2007.4373773},
file = {Mattmann2007-P_ISWC.pdf:Mattmann2007-P_ISWC.pdf:PDF},
owner = {oam},
timestamp = {2007/10/09}
}
Generated by bib2html.pl on Wed Dec 01, 2010 23:40:25