Inference Engine Based on a Hierarchical Structure for Detecting Everyday Activities Within the Home

Author:

Naeem Usman,Tawil Abdel-Rahman,Semelis Ivans,Azam Muhammad Awais,Ghazanfar Mustansar Ali

Publisher

Springer International Publishing

Reference28 articles.

1. Rutherford, T.: Population Ageing: Statistics. Commons Library Standard Note (2012). http://www.parliament.uk/briefing-papers/sn03228 . Accessed 12 Aug 2015

2. Katz, S., Ford, A.B., Moskowitz, R.W., Jackson, B.A., Jaffe, M.W.: Studies of illness in the aged: the index of ADL: a standardized measure of biological and psychosocial function. J. Am. Med. Assoc. 185(12), 914–919 (1963)

3. Fleury, A., Vacher, M., Noury, N.: SVM-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results. IEEE Trans. Inf. Technol. Biomed. 14(2), 274–283 (2010)

4. Wilson, D.H., Atkeson, C.: Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) Proceedings of the Third International Conference on Pervasive Computing, PERVASIVE 2005, pp. 62–79. Springer, Berlin (2005)

5. Kern, N., Schiele, B., Schmidt, A.: Multi-sensor activity context detection for wearable computing. In: Ambient Intelligence. Lecture Notes in Computer Science, vol. 2875, pp. 220–232 (2003)

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