Abstract
This paper develops an algorithm for robust human activity recognition in the face of imprecise sensor placement. It is motivated by the emerging body sensor networks that monitor human activities (as opposed to environmental phenomena) for medical, entertainment, health-and-wellness, training, assisted-living, or entertainment reasons. Activities such as sitting, writing, and walking have been successfully inferred from data provided by body-worn accelerometers. A common concern with previous approaches is their sensitivity with respect to sensor placement. This paper makes two contributions. First, we explicitly address robustness of human activity recognition with respect to changes in accelerometer orientation. We develop a novel set of features based on relative activity-specific body-energy allocation and successfully apply them to recognize human activities in the presence of imprecise sensor placement. Second, we evaluate the accuracy of the approach using empirical data from body-worn sensors.
This work is supported in part by NSF grant CNS 06-15318, CNS 05-5759 and the Vietnam Education Foundation (VEF).
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References
Xie, L., Chang, S.F., Divakaran, A., Sun, H.: Unsupervised Mining of Statistical Temporal Structures in Video, pp. 280–307. Kluwer Academic Publishers, New York (2003)
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: AAAI, California, pp. 1541–1546. AAAI Press / The MIT Press (2005)
Bachmann, E.R., Yun, X., McGhee, R.B.: Sourceless tracking of human posture using small inertial/magnetic sensors. In: Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, July 2003, vol. 2, pp. 822–829 (2003)
Antonsson, E.K., Mann, R.W.: The frequency of gait. Journal of Biomechanics 18(1), 39–47 (1985)
Sun, M.S., Hill, J.O.: A method for measuring mechanical work. Journal of Biomechanics 26, 229–241 (1993)
Bar-Shalom, Y., Li, X.R.: Estimation with Applications to Tracking and Navigation. John Wiley & Sons, Inc., New York (2001)
Ganti, R.K., Jayachandran, P., Abdelzaher, T.F., Stankovic, J.A.: Satire: A software architecture for smart attire. In: MobiSys 2006: Proceedings of the 4th International Conference on Mobile Systems, Applications and Services, pp. 110–123 (2006)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Van Rijsbergen, C.J.: Information Retrieval. 2 edn. Dept. of Computer Science, University of Glasgow, London (1979)
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Pham, N., Abdelzaher, T. (2008). Robust Dynamic Human Activity Recognition Based on Relative Energy Allocation. In: Nikoletseas, S.E., Chlebus, B.S., Johnson, D.B., Krishnamachari, B. (eds) Distributed Computing in Sensor Systems. DCOSS 2008. Lecture Notes in Computer Science, vol 5067. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69170-9_39
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DOI: https://doi.org/10.1007/978-3-540-69170-9_39
Publisher Name: Springer, Berlin, Heidelberg
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