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Nowadays, basketball is one of the most entertaining and popular sports. In the last years, the number of people that are dedicating themselves to basketball has grown rapidly. The increasing number of sportsmen defines the increasing demand to monitor and analyse their performance, hereby granting the possibility to review and evaluate mistakes made within different game phases, which, in turn, would be useful for future training. The present research is the first step to develop a wireless system (Smart Basketball Glove (SBG)) for basketball shot analysis and training. SBG system is based on knitted tension and pressure sensors that were already successfully used in Smart Socks and Smart Shirt applications. These sensors, while embedded into the proposed system’s textile part, showed high tactile sensitivity and speed of response and, therefore, demonstrates potential abilities to analyse the wrist and fingers movement and estimate the forces with which fingers interact with the ball during basketball shot. Necessary requirements for data acquisition and transition device of SBG are formulated for further system’s development as well.

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.

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.

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.