Slice&Dice: Recognizing Food Preparation Activities Using Embedded Accelerometers

  • Cuong Pham
  • Patrick Olivier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5859)


Within the context of an endeavor to provide situated support for people with cognitive impairments in the kitchen, we developed and evaluated classifiers for recognizing 11 actions involved in food preparation. Data was collected from 20 lay subjects using four specially designed kitchen utensils incorporating embedded 3-axis accelerometers. Subjects were asked to prepare a mixed salad in our laboratory-based instrumented kitchen environment. Video of each subject’s food preparation activities were independently annotated by three different coders. Several classifiers were trained and tested using these features. With an overall accuracy of 82.9% our investigation demonstrated that a broad set of food preparation actions can be reliably recognized using sensors embedded in kitchen utensils.


Activity Recognition Food Preparation Ambient Intelligence Human Activity Recognition Impaired User 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Weiser, M.: The computer for the 21st century. Scientific American 265(3), 93–104 (1991)CrossRefGoogle Scholar
  2. 2.
    Wherton, J., Monk, A.: Designing cognitive supports for dementia. SIGACCESS Access. Comput. (86), 28–31 (2006)Google Scholar
  3. 3.
    Wherton, J., Monk, A.: Technological opportunities for supporting people with dementia who are living at home. International Journal of Human-Computer Studies 66(8), 571–586 (2008)CrossRefGoogle Scholar
  4. 4.
    Mihailidis, A., Boger, J., Canido, M., Hoey, J.: The use of an intelligent prompting system for people with dementia. interactions 14(4), 34–37 (2007)CrossRefGoogle Scholar
  5. 5.
    Tapia, E.M., Intille, S.S., Haskell, W., Larson, K., Wright, J., King, A., Friedman, R.: Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In: Proceedings of 11th IEEE International Symposium on Wearable Computers, October 2007, pp. 37–40 (2007)Google Scholar
  6. 6.
    Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)Google Scholar
  7. 7.
    Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity recognition from accelerometer data. In: Proceedings of the Seventeenth Conference on Innovative Applications of Artificial Intelligence (IAAI), pp. 1541–1546. AAAI Press, Menlo Park (2005)Google Scholar
  8. 8.
    Wu, J., Osuntogun, A., Choudhury, T., Philipose, M., Rehg, J.M.: A scalable approach to activity recognition based on object use. In: Proceedings of the International Conference on Computer Vision (ICCV), Rio de (2007)Google Scholar
  9. 9.
    Huynh, T., Blanke, U., Schiele, B.: Scalable recognition of daily activities with wearable sensors. In: Hightower, J., Schiele, B., Strang, T. (eds.) LoCA 2007. LNCS, vol. 4718, pp. 50–67. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    Philipose, M., Fishkin, K.P., Perkowitz, M., Patterson, D.J., Fox, D., Kautz, H., Hahnel, D.: Inferring activities from interactions with objects. IEEE Pervasive Computing 3(4), 50–57 (2004)CrossRefGoogle Scholar
  12. 12.
    Stikic, M., Huynh, T., Van Laerhoven, K., Schiele, B.: Adl recognition based on the combination of rfid and accelerometer sensing. In: Second International Conference on Pervasive Computing Technologies for Healthcare. PervasiveHealth 2008, 30 2008-February 1 2008, pp. 258–263 (2008)Google Scholar
  13. 13.
    Kim, I., Im, S., Hong, E., Ahn, S.C., Kim, H.-G.: ADL classification using triaxial accelerometers and RFID. In: Proceedings of the International Workshop on Ubiquitous Convergence Technology (November 2007)Google Scholar
  14. 14.
    Wang, S., Yang, J., Chen, N., Chen, X., Zhang, Q.: Human activity recognition with user-free accelerometers in the sensor networks. In: International Conference on Neural Networks and Brain (ICNN&B 2005), October 2005, vol. 2, pp. 1212–1217 (2005)Google Scholar
  15. 15.
    Robertson, N., Reid, I.: A general method for human activity recognition in video. Computer Vision and Image Understanding 104(2-3), 232–248 (2006); Special Issue on Modeling People: Vision-based understanding of a person’s shape, appearance, movement and behaviourCrossRefGoogle Scholar
  16. 16.
  17. 17.
    Olivier, P., Xu, G., Monk, A., Hoey, J.: Ambient kitchen: designing situated services using a high fidelity prototyping environment. In: PETRA 2009: Proceedings of the 2nd International Conference on PErvsive Technologies Related to Assistive Environments, pp. 1–7. ACM, New York (2009)CrossRefGoogle Scholar
  18. 18.
    Kipp, M.: Anvil – a generic annotation tool for multimodal dialogue. In: Proceedings of In EUROSPEECH 2001, pp. 1367–1370 (2001)Google Scholar
  19. 19.
    Shannon, C.E.: A mathematical theory of communication. SIGMOBILE Mob. Comput. Commun. Rev. 5(1), 3–55 (2001)CrossRefGoogle Scholar
  20. 20.
  21. 21.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Cuong Pham
    • 1
  • Patrick Olivier
    • 1
  1. 1.Culture Lab, School of Computing ScienceNewcastle University 

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