Abstract
The development of a system for classifying and interpreting human hands motion is considered in this paper. This is obtained by locally approximating motion data with rank-1 structures. The approximation is obtained in two steps: first the time series is decomposed into simpler sub-series (segmentation), then each subseries labelled by a unique vector.
The effectiveness of the proposed strategy is shown on sensory data from a data-glove when a human picks a tin can and a pencil. The strategy proves to be simple and reliable, even in the presence of unknown data corrupted by noise, and can be used as a basis for real-time automated recognition and interpretation of human gesture.
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References
Cédras, C., Shah, M.A.: Motion Based Recognition: A Survey. Image and Vision Computing 13(2), 129–155 (1995)
Buxton, H.: Learning and understanding dynamic scene activity: a review. Image Vision Comput. 21(1), 125–136 (2003)
Poppe, R.: A survey on vision-based human action recognition. Image Vision Comput. 28(6), 976–990 (2010)
Sturman, D.J., Zeltzer, D.: A Survey of Glove-based Input. IEEE Comput. Graph. Appl. 14(1), 30–39 (1994)
Kessler, G.D., Hodges, L.F., Walker, N., Neff: Evaluation of the CyberGlove as a whole-hand input device. ACM Trans. Comput.-Hum. Interact. 2(4), 263–283 (1995)
Santello, M., Flanders, M., Soechting, J.F.: Postural synergies for tool use. Journal of Neuroscience 18, 10105–10115 (1998)
Fod, A., Matarić, M.J., Jenkins, C.: Automated Derivation of Primitives for Movement Classification. Autonomous Robots 12, 39–54 (2002)
Gould, K., Rangarajan, K., Shah, M.: Detection and representation of events in motion trajectories. In: Gonzalez, Mahdavieh (eds.) Advances in Image Processing and Analysis. Optical Engineering Press (1992)
Nakazawa, A., Nakaoka, S., Shiratori, T., Ikeuchi, K.: Analysis and synthesis of human dance motions. In: Proc. of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 83–88 (2003)
Keogh, E., Chu, S., Hart, D., Pazzani, M.: An online algorithm for segmenting time series. In: Proceedings 2001 IEEE International Conference on Data Mining, pp. 289–296 (2001)
Fuchs, E., Gruber, T., Nitschke, J., Sick, B.: Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations. IEEE Trans. on Pattern Analysis and Machine Intelligence 32(12), 2232–2245 (2010)
Wang, Z.J., Willett, P.: Joint segmentation and classification of time series using class-specific features. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 1056–1067 (2004)
Ng, C.W., Ranganath, S.: Real-time gesture recognition system and application. Image and Vision Computing 20(13-14), 993–1007 (2002)
Cavallo, A.: Primitive Action Extraction for a Human Hand by using SVD. In: 9th IEEE International Symposium on Intelligent Systems and Informatics (SISY 2011), Subotica, Serbia (September 2011)
Cavallo, A.: Using SVD for Segmentation and Classification of Human Hand Actions. In: Tenth IEEE International Conference on Machine Learning and Applications (ICMLA 2011), Honolulu, Hawaii (December 2011)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer (2003)
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Cavallo, A. (2012). Real-Time Recognizing Human Hand Gestures. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7508. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33503-7_21
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DOI: https://doi.org/10.1007/978-3-642-33503-7_21
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