Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Weiser, M.: The computer for the 21st century. Scientific American 265(3), 93–104 (1991)
Wherton, J., Monk, A.: Designing cognitive supports for dementia. SIGACCESS Access. Comput. (86), 28–31 (2006)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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 behaviour
Wii Remote: http://en.wikipedia.org/wiki/Wii_Remote
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)
Kipp, M.: Anvil – a generic annotation tool for multimodal dialogue. In: Proceedings of In EUROSPEECH 2001, pp. 1367–1370 (2001)
Shannon, C.E.: A mathematical theory of communication. SIGMOBILE Mob. Comput. Commun. Rev. 5(1), 3–55 (2001)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pham, C., Olivier, P. (2009). Slice&Dice: Recognizing Food Preparation Activities Using Embedded Accelerometers. In: Tscheligi, M., et al. Ambient Intelligence. AmI 2009. Lecture Notes in Computer Science, vol 5859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05408-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-05408-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05407-5
Online ISBN: 978-3-642-05408-2
eBook Packages: Computer ScienceComputer Science (R0)