Activity Recognition in Pervasive Intelligent Environments

Volume 4 of the series Atlantis Ambient and Pervasive Intelligence pp 313-329


Activity Recognition and Healthier Food Preparation

  • Thomas PlötzAffiliated withSchool of Computing Science, Newcastle University Email author 
  • , Paula MoynihanAffiliated withInstitute for Ageing and Health, Newcastle University
  • , Cuong PhamAffiliated withSchool of Computing Science, Newcastle University
  • , Patrick OlivierAffiliated withSchool of Computing Science, Newcastle University

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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.