THE GOOD, THE BAD AND THE UNRECOGNIZED: SMART TEXTILE SIGNAL CLUSTERING BY SELF-ORGANIZING MAP

Artyom Shootov, Yuriy Chizhov, Alexander Oks, Alexei Katashev

Abstract


The present article is a series of publications dedicated to the research of smart fabric sensors integrated into socks and is also part of the project aimed at developing the measuring system based on smart fabric supplied with sensors and intellectual data processing. The aim of the article is to perform a practical study on the application of Self-Organizing Map to smart textile signal clustering. Within the framework of the research, different approaches to the organization of network training are explored. A method for encoding an input pattern is also proposed. It has been established that the network is able to recognize the signal as a good step, a bad step, and an unrecognized step. The primary classification allows further selecting specific algorithms for a detailed analysis of good steps and bad steps. The detailed analysis of bad steps is the key to solving the problem of revealing of an athlete’s special type of fatigue, leading to injuries.

Keywords


DAid® Pressure Sock System; self-organizing map; smart textile signal clustering

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References


Oks A., Katashev A., Zadinans M., Rancans M., Litvak J. Development of Smart Sock System for Gate Analysis and Foot Pressure Control. XIV Mediterranean Conference on Medical and Biological Engineering and Computing. 2016, pp. 472-475.

Hreljac A. Impact and overuse injuries in runners. Med Sci Sports Exerc. Vol. 36, No. 5, 2004, pp. 845-849.

Kong K., Tomizuka M. Smooth and Continuous Human Gait Phase Detection Based on Foot Pressure Patterns. IEEE International Conference on Robotics and Automation, 2008. pp. 3678-3683.

Kong K., Bae J., Tomizuka M. Detection of Abnormalities in a Human Gait Using Smart Shoes. SPIE Smart Structures/NDE, Health Monitoring. 2008.

Srivises W., Nilkhamhang, I., Tungpimolrut, K. Design of a Smart Shoe for Reliable Gait Analysis Using State Transition Theory. In Proceedings of 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. 2012, pp. 1 4.

Srivises W., Nilkhamhang, I., Tungpimolrut, K. Design of a Smart Shoe for Reliable Gait Analysis Using Fuzzy Logic. Proceedings of SICE Annual Conference (SICE) 2012. 2012, pp. 834-838.

J.Bae, M.Tomizuka. Gait Phase Analysis Based on a Hidden Markov Model. Mechatronics. Vol. 21, no. 6, 2011, pp. 961 970.

Kohonen T., Honkela T. Kohonen Network. 2007, Scholarpedia, 2(1):1568.

Shootov A., Chizhov Y., Aleksejeva L., Oks A., Katashev A. Artificial Neural Network Based Approach for Control Points Detection in Smart Textile Signals. Procedia Computer Science. Vol. 104, 2017, pp. 548




DOI: http://dx.doi.org/10.17770/etr2017vol2.2567

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