Abstract
This paper presents an approach to recognition of human actions such as sitting, standing, walking or running by analysing the data produced by the sensors of a smart phone. The data comes as streams of parallel time series from 21 sensors. We have used genetic programming to evolve detectors for a number of actions and compared the detection accuracy of the evolved detectors with detectors built from the classical machine learning methods including Decision Trees, Naïve Bayes, Nearest Neighbour and Support Vector Machines. The evolved detectors were considerably more accurate. We conclude that the proposed GP method can capture complex interaction of variables in parallel time series without using predefined features.
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References
Aha, D., Kibler, D., Albert, M.: Instance-based learning algorithms. Machine Learning 6(1), 37–66 (1991)
Hetland, M.L., Sætrom, P.: Temporal rule discovery using genetic programming and specialized hardware. In: Proc. of the 4th Int. Conf. on Recent Advances in Soft Computing, pp. 182–188 (2002)
John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, UAI 1995, pp. 338–345. Morgan Kaufmann Publishers Inc., San Francisco (1995)
Kaboudan, M.: Spatiotemporal forecasting of housing prices by use of genetic programming. In: The 16th Annual Meeting of the Association of Global Business (2004)
Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K.: Improvements to platt’s smo algorithm for svm classifier design. Neural Comput. 13(3), 637–649 (2001)
Keogh, E., Lin, J., Fu, A.: Hot sax: Efficiently finding the most unusual time series subsequence. In: Proceedings of the Fifth IEEE International Conference on Data Mining, ICDM 2005, pp. 226–233. IEEE Computer Society, Washington, DC (2005)
Kishore, J.K., Patnaik, L.M., Mani, V., Agrawal, V.K.: Application of genetic programming for multicategory pattern classification. Trans. Evol. Comp. 4(3), 242–258 (2000)
Loveard, T., Ciesielski, V.: Representing classification problems in genetic programming. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 2, pp. 1070–1077. IEEE (2001)
Muni, D.P., Pal, N.R., Das, J.: A novel approach to design classifiers using genetic programming. Trans. Evol. Comp. 8(2), 183–196 (2004)
Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods, pp. 185–208. MIT Press, Cambridge (1999)
Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Ratanamahatana, C., Lin, J., Gunopulos, D., Keogh, E., Vlachos, M., Das, G.: Mining time series data. In: Data Mining and Knowledge Discovery Handbook, pp. 1049–1077 (2010)
Song, A., Pinto, B.: Study of gp representations for motion detection with unstable background. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)
Wagner, N., Michalewicz, Z.: An analysis of adaptive windowing for time series forecasting in dynamic environments: further tests of the dyfor gp model. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO 2008, pp. 1657–1664. ACM, New York (2008)
Wang, L., Gu, T., Tao, X., Chen, H., Lu, J.: Recognizing multi-user activities using wearable sensors in a smart home. Pervasive Mob. Comput. 7(3), 287–298 (2011)
Xie, F., Song, A., Ciesielski, V.: Event detection in time series by genetic programming. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (June 2012)
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Xie, F., Song, A., Ciesielski, V. (2013). Human Action Recognition from Multi-Sensor Stream Data by Genetic Programming. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_42
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DOI: https://doi.org/10.1007/978-3-642-37192-9_42
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