Behavior Analysis Based on Coordinates of Body Tags
This paper describes fall detection, activity recognition and the detection of anomalous gait in the Confidence project. The project aims to prolong the independence of the elderly by detecting falls and other types of behavior indicating a health problem. The behavior will be analyzed based on the coordinates of tags worn on the body. The coordinates will be detected with radio sensors. We describe two Confidence modules. The first one classifies the user’s activity into one of six classes, including falling. The second one detects walking anomalies, such as limping, dizziness and hemiplegia. The walking analysis can automatically adapt to each person by using only the examples of normal walking of that person. Both modules employ machine learning: the paper focuses on the features they use and the effect of tag placement and sensor noise on the classification accuracy. Four tags were enough for activity recognition accuracy of over 93% at moderate sensor noise, while six were needed to detect walking anomalies with the accuracy of over 90%.
KeywordsActivity recognition fall detection gait machine learning
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- 2.Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: LOF: Identifying density-based local outliers. In: 2000 ACM SIGMOD International Conference on Management of Data, pp. 93–104 (2000)Google Scholar
- 3.Confidence: Ubiquitous Care System to Support Independent Living, http://www.confidence-eu.org
- 4.Craik, R., Oatis, C.: Gait Analysis: Theory and Application. Mosby-Year Book (1995)Google Scholar
- 8.Luštrek, M., Kaluža, B.: Fall detection and activity recognition with machine learning. Informatica 33(2), 205–212 (2009)Google Scholar
- 9.Paróczai, R., Bejek, Z., Illyés, Á., Kocsis, L., Kiss, R.M.: Gait parameters of healthy, elderly people. Facta Universitatis 4(1), 49–58 (2006)Google Scholar
- 10.Sukthankar, G., Sycara, K.: A cost minimization approach to human behavior recognition. In: The Fourth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), pp. 1067–1074 (2005)Google Scholar
- 11.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: The 6th International Semantic Web Conference, pp. 37–40 (2007)Google Scholar
- 13.Zhang, T., Wang, J., Liu, P., Hou, J.: Fall detection by wearable sensor and One-Class SVM algorithm. In: Nossum, R.T. (ed.) ACAI 1987. LNCS, vol. 345, pp. 858–863. Springer, Heidelberg (1988)Google Scholar