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2011 International Conference on Body Sensor Networks
eCushion: An eTextile Device for Sitting Posture Monitoring
Dallas, Texas USA
May 23-May 25
ISBN: 978-0-7695-4431-1
| ASCII Text | x | ||
| Wenyao Xu, Zhinan Li, Ming-Chun Huang, Navid Amini, Majid Sarrafzadeh, "eCushion: An eTextile Device for Sitting Posture Monitoring," Wearable and Implantable Body Sensor Networks, International Workshop on, pp. 194-199, 2011 International Conference on Body Sensor Networks, 2011. | |||
| BibTex | x | ||
| @article{ 10.1109/BSN.2011.24, author = {Wenyao Xu and Zhinan Li and Ming-Chun Huang and Navid Amini and Majid Sarrafzadeh}, title = {eCushion: An eTextile Device for Sitting Posture Monitoring}, journal ={Wearable and Implantable Body Sensor Networks, International Workshop on}, volume = {0}, year = {2011}, isbn = {978-0-7695-4431-1}, pages = {194-199}, doi = {http://doi.ieeecomputersociety.org/10.1109/BSN.2011.24}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Wearable and Implantable Body Sensor Networks, International Workshop on TI - eCushion: An eTextile Device for Sitting Posture Monitoring SN - 978-0-7695-4431-1 SP194 EP199 A1 - Wenyao Xu, A1 - Zhinan Li, A1 - Ming-Chun Huang, A1 - Navid Amini, A1 - Majid Sarrafzadeh, PY - 2011 VL - 0 JA - Wearable and Implantable Body Sensor Networks, International Workshop on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/BSN.2011.24
Sitting posture analysis is critical for daily applications in biomedical, education and healthcare fields. However, it remains unclear how to monitor sitting posture economically and comfortably. To this end, we presented an eTextile device, called eCushion, in this paper, which can analyze the sitting posture of human being accurately and non-invasively. First, we discussed the implementation of eCushion and design challenges of sensing data, such as scale, offset, rotation and crosstalk. Then, several effective techniques have been proposed to improve the recognition rate of sitting posture. Our experimental results show that the recognition rate of our eCushion system could achieve 92% for object-oriented cases and 79% for general cases.
Citation:
Wenyao Xu, Zhinan Li, Ming-Chun Huang, Navid Amini, Majid Sarrafzadeh, "eCushion: An eTextile Device for Sitting Posture Monitoring," bsn, pp.194-199, 2011 International Conference on Body Sensor Networks, 2011
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