Other formats:
BibTeX
LaTeX
RIS
@inproceedings{1196531,
author = {Wang, Rui and Sitová, Zdeňka and Jia, Xiaoqing and He, Xiang and Abramson, Tobi and Gasti, Paolo and Balagani, Kiran S. and Farajidavar, Aydin},
address = {New York},
booktitle = {36th Annual International IEEE Engineering in Medicine and Biology Society Conference (EMBS), 2014},
doi = {http://dx.doi.org/10.1109/EMBC.2014.6944542},
keywords = {ADHERENCE; DRUG},
howpublished = {tištěná verze "print"},
language = {eng},
location = {New York},
isbn = {978-1-4244-7929-0},
pages = {4168-4171},
publisher = {IEEE},
title = {Automatic Identification of Solid-Phase Medication Intake Using Wireless Wearable Accelerometers},
url = {http://tweetleveluat.edelman.com/event/embc-2014/paper-details?pdID=1527},
year = {2014}
}
TY - JOUR
ID - 1196531
AU - Wang, Rui - Sitová, Zdeňka - Jia, Xiaoqing - He, Xiang - Abramson, Tobi - Gasti, Paolo - Balagani, Kiran S. - Farajidavar, Aydin
PY - 2014
TI - Automatic Identification of Solid-Phase Medication Intake Using Wireless Wearable Accelerometers
PB - IEEE
CY - New York
SN - 9781424479290
KW - ADHERENCE
KW - DRUG
UR - http://tweetleveluat.edelman.com/event/embc-2014/paper-details?pdID=1527
N2 - We have proposed a novel solution to a fundamental problem encountered in implementing non-ingestion based medical adherence monitoring systems, namely, how to reliably identify pill medication intake. We show how wireless wearable devices with tri-axial accelerometer can be used to detect and classify hand gestures of users during solid-phase medication intake. Two devices were worn on the wrists of each user. Users were asked to perform two activities in the way that is natural and most comfortable to them: (1) taking empty gelatin capsules with water, and (2) drinking water and wiping mouth. 25 users participated in this study. The signals obtained from the devices were filtered and the patterns were identified using dynamic time warping algorithm. Using hand gesture signals, we achieved 84.17 percent true positive rate and 13.33 percent false alarm rate, thus demonstrating that the hand gestures could be used to effectively identify pill taking activity.
ER -
WANG, Rui, Zdeňka SITOVÁ, Xiaoqing JIA, Xiang HE, Tobi ABRAMSON, Paolo GASTI, Kiran S. BALAGANI and Aydin FARAJIDAVAR. Automatic Identification of Solid-Phase Medication Intake Using Wireless Wearable Accelerometers. In \textit{36th Annual International IEEE Engineering in Medicine and Biology Society Conference (EMBS), 2014}. New York: IEEE, 2014, p.~4168-4171. ISBN~978-1-4244-7929-0. Available from: https://dx.doi.org/10.1109/EMBC.2014.6944542.
|
