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Recognizing Upper Body Postures using Textile Strain Sensors

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5 Author(s)
Corinne Mattmann ; ETH Zurich, Wearable Computing Lab, ; Oliver Amft ; Holger Harms ; Gerhard Troster
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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.

Published in:

2007 11th IEEE International Symposium on Wearable Computers

Date of Conference:

11-13 Oct. 2007