Abstract
The paper presents an idea of the method of creating the signal classifier which is based on the optimization of the metric (distance) function. The authors suggest that the proper choice of metric function parameters allows to adapt the whole classification operation to solve certain problems of the time-varied signal recognition, especially in medical applications. The main advantage of the described approach is a possibility to interpret the obtained solutions. This may enable to progress the doctor’s skills, as well as improve the automatic classification method. The paper presents a brief example of the method usage in a practical application. It deals with the classification of the signals obtained from MEMS (3-axis accelerometer) sensors during the Lachman knee test. The authors point to main conditions which determine an increase in the efficiency of the described approach. Particularly, they are involved in developing efficient optimization methods of discontinuous criterion functions and algorithms for detection the cohesive group of points that define the relevant signal regions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
More precisely, time-varied signal y(t) is scaled and shifted according to following rule: \(\mathrm {scale}(y(t)) = s_y \, y(s_t t+d_t)+d_y\).
References
Tadeusiewicz, R.: Automatic understanding of signals. In: Klopotek, A., Wierzchoń, S., Trojanowski, K. (eds.) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol. 25, pp. 577–590. Springer, Heidelberg (2004)
Mitchell, T.M.: Machine Learning. McGraw-Hill Science, New York (1997)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1995)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons, New York (2000)
Piekarczyk, M., Ogiela, M.R.: Hierarchical graph-grammar model for secure and efficient handwritten signatures classification. J. Univ. Comput. Sci. 17(6), 926–943 (2011)
Tadeusiewicz, R., Ogiela, M.R.: Medical Image Understanding Technology. Studies in Fuzziness and Soft Computing, vol. 156. Springer, Heidelberg (2004)
Piekarczyk, M., Ogiela, M.R.: Matrix-based hierarchical graph matching in off-line handwritten signatures recognition. In: Proceedings of 2nd IAPR Asian Conference on Pattern Recognition, IEEE (2013)
Michalski, R.S., Steep, R.: Learning from observation: conceptual clustering. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning: An Artificial Intelligence Approach, vol. 2. Morgan Kaufmann, San Mateo (1986)
Wójcik, K.: Hierarchical knowledge structure applied to image analyzing system - possibilities of practical usage. In: Tjoa, A.M., Quirchmayr, G., You, I., Xu, L. (eds.) Availability, Reliability and Security for Business, Enterprise and Health Information Systems. LNCS, pp. 149–163. Springer, Heidelberg (2011)
Wójcik, K.: Knowledge transformations applied in image classification task. In: Choraś, R.S. (ed.) Image Processing and Communications Challenges 5. Advanced in Intelligent Systems and Computing, vol. 233, pp. 115–123. Springer, Heidelberg (2013)
Adistambha, K., Ritz, C.H., Burnett, I.S.: Motion classification using dynamic time warping. In: International Workshop on Multimedia Signal Processing, Wollongong (2008)
Wójcik, K., Piekarczyk, M., Golec, J., Szczygiel, E.: Simple system for supporting learning of human motion capabilities. Appl. Mech. Mater. 555, 673–680 (2014)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Englewood Cliffs (2010)
Labbé, D.R., Li, D., Grimard, G., de Guise, J.A., Hagemester, N.: Quantitative pivot shift assessment using combined inertial and magnetic sensing. Knee Surg. Sports Traumatol. Arthroscopy 23(8), 2330–2338 (2015). Springer-Verlag, Berlin, Heidelberg
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Wójcik, K., Wziętek, B., Wziętek, P., Piekarczyk, M. (2016). Signal Recognition Based on Multidimensional Optimization of Distance Function in Medical Applications. In: Gaj, P., Kwiecień, A., Stera, P. (eds) Computer Networks. CN 2016. Communications in Computer and Information Science, vol 608. Springer, Cham. https://doi.org/10.1007/978-3-319-39207-3_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-39207-3_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39206-6
Online ISBN: 978-3-319-39207-3
eBook Packages: Computer ScienceComputer Science (R0)