TY - GEN
T1 - Computing with uncertainty in a smart textile surface for object recognition
AU - Rofouei, Mahsan
AU - Xu, Wenyao
AU - Sarrafzadeh, Majid
PY - 2010
Y1 - 2010
N2 - A wearable surface capable of performing object recognition on objects placed on it has many applications in health care such as surgery, assisted living posture monitoring, specifically movement of body parts during sleep and etc. The flexibility and wearability of textile material allows its widespread applications in body-worn contexts. In this work, we propose a portable and wearable smart textile surface which is capable of performing object recognition on a set of prior known objects. We integrate data from multiple sensors to gain knowledge about objects in the environment. The uncertainty present in such systems can lead to inaccurate interpretation of the data which is crucial in various medical applications. The most significant part of this uncertainty is due to effects of multiple sensors on each other. We look at different sources of uncertainties in such systems and formulate them. We modify vision algorithm to account for these uncertainties and in the end we present precision bounds for the accuracy of the system
AB - A wearable surface capable of performing object recognition on objects placed on it has many applications in health care such as surgery, assisted living posture monitoring, specifically movement of body parts during sleep and etc. The flexibility and wearability of textile material allows its widespread applications in body-worn contexts. In this work, we propose a portable and wearable smart textile surface which is capable of performing object recognition on a set of prior known objects. We integrate data from multiple sensors to gain knowledge about objects in the environment. The uncertainty present in such systems can lead to inaccurate interpretation of the data which is crucial in various medical applications. The most significant part of this uncertainty is due to effects of multiple sensors on each other. We look at different sources of uncertainties in such systems and formulate them. We modify vision algorithm to account for these uncertainties and in the end we present precision bounds for the accuracy of the system
UR - https://www.scopus.com/pages/publications/78649307557
U2 - 10.1109/MFI.2010.5604473
DO - 10.1109/MFI.2010.5604473
M3 - Conference contribution
AN - SCOPUS:78649307557
SN - 9781424454266
T3 - IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
SP - 174
EP - 179
BT - 2010 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2010
T2 - 2010 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2010
Y2 - 5 September 2010 through 7 September 2010
ER -