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Lookup NU author(s): Dr Thomas Ploetz, Professor Paula Moynihan, Cuong Pham, Professor Patrick OlivierORCiD
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Obesity is an increasing problem for modern societies, which implies enormous financial burdens for public health-care systems. There is growing evidence that a lack of cooking and food preparation skills is a substantial barrier to healthier eating for a significant proportion of the population. We present the basis for a technological approach to promoting healthier eating by encouraging people to cook more often. We integrated tri-axial acceleration sensors into kitchen utensils (knifes, scoops, spoons), which allows us to continuously monitor the activities people perform while acting in the kitchen. A recognition framework is described, which discriminates ten typical kitchen activities. It is based on a sliding-window procedure that extracts statistical features for contiguous portions of the sensor data. These frames are fed into a Gaussian mixture density classifier, which provides recognition hypotheses in real-time. We evaluated the activity recognition system by means of practical experiments of unconstrained food preparation. The system achieves classification accuracy of ca. 90% for a dataset that covers 20 persons’ cooking activities.
Author(s): Ploetz T, Moynihan P, Pham C, Olivier P
Editor(s): Chen, L., Nugent, C.D., Biswas, J., Hoey, J.
Publication type: Book Chapter
Publication status: Published
Book Title: Activity Recognition in Pervasive Intelligent Environments
Year: 2011
Pages: 313-339
Edition: 1st
Series Title: Atlantis Ambient and Pervasive Intelligence
Publisher: Atlantis Press
Place Published: Amsterdam, Netherlands
URL: http://dx.doi.org/10.2991/978-94-91216-05-3_14
DOI: 10.2991/978-94-91216-05-3_14
Library holdings: Search Newcastle University Library for this item
ISBN: 9789078677420