O. Amft, M. Kusserow, and G. Tröster. BSN 2007: Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks, 13, page 242--247. IFMBE Proceedings, Springer, (March 2007)
DOI: 10.1007/978-3-540-70994-7_41
Abstract
Dietary behaviour is an important lifestyle aspect and directly related to long-term health.We present an approach to detect eating and drinking intake cycles from body-worn sensors. Information derived from the sensors are considered as abstract activity events and a sequence modelling is applied utilising probabilistic context-free grammars. Different grammar models are discussed and applied to dietary intake evaluation data. The detection performance for different foods and food categories is reported. We show that the approach is a feasible strategy to segment dietary intake cycles and identify the food category.
%0 Conference Paper
%1 Amft2007-P_BSN
%A Amft, Oliver
%A Kusserow, Martin
%A Tröster, Gerhard
%B BSN 2007: Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
%D 2007
%E Leonhardt, Steffen
%E Falck, Thomas
%E Mähönen, Petri
%I Springer
%K detection dietary eating intake monitoring nutrition parsing
%P 242--247
%R 10.1007/978-3-540-70994-7_41
%T Probabilistic parsing of dietary activity events
%V 13
%X Dietary behaviour is an important lifestyle aspect and directly related to long-term health.We present an approach to detect eating and drinking intake cycles from body-worn sensors. Information derived from the sensors are considered as abstract activity events and a sequence modelling is applied utilising probabilistic context-free grammars. Different grammar models are discussed and applied to dietary intake evaluation data. The detection performance for different foods and food categories is reported. We show that the approach is a feasible strategy to segment dietary intake cycles and identify the food category.
@inproceedings{Amft2007-P_BSN,
abstract = {Dietary behaviour is an important lifestyle aspect and directly related to long-term health.We present an approach to detect eating and drinking intake cycles from body-worn sensors. Information derived from the sensors are considered as abstract activity events and a sequence modelling is applied utilising probabilistic context-free grammars. Different grammar models are discussed and applied to dietary intake evaluation data. The detection performance for different foods and food categories is reported. We show that the approach is a feasible strategy to segment dietary intake cycles and identify the food category.},
added-at = {2009-04-27T02:46:34.000+0200},
author = {Amft, Oliver and Kusserow, Martin and Tr\"oster, Gerhard},
biburl = {https://www.bibsonomy.org/bibtex/2d5d94efdd192a6adfd880a65cd1e372c/aihec},
booktitle = {BSN 2007: Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks},
doi = {10.1007/978-3-540-70994-7_41},
editor = {Leonhardt, Steffen and Falck, Thomas and M\"ah\"onen, Petri},
interhash = {0a2c7449ef73744c01e48dac1cf6223e},
intrahash = {d5d94efdd192a6adfd880a65cd1e372c},
keywords = {detection dietary eating intake monitoring nutrition parsing},
month = {March},
organization = {IFMBE Proceedings},
owner = {oam},
pages = {242--247},
pdf = {Amft2007-P_BSN.pdf},
publisher = {Springer},
timestamp = {2009-04-27T02:46:34.000+0200},
title = {Probabilistic parsing of dietary activity events},
volume = 13,
year = 2007
}