Application of intelligent digital infrastructure into the L-test implementation in the physical education of students with lower limb ambulance

Abstract

The purpose of the study was to determine the psychometric properties of the L-test for students with lower limb amputation implemented by intelligent digital infrastructure. Material and Methods. The experiment involved first-year students (males) with amputation of the lower left limb in the absence of acute conditions, open wounds, or complications. The theoretical and empirical research used the following methods: analysis, synthesis, systematization, generalization, measurement and mathematical statistics. Measurement was implemented using the L-test. Results. The result of our scientific search was the development of an intelligent digital infrastructure designed for the implementation of the L-test, which involved solving tasks in collecting and analyzing testing data such as test execution time, gait trajectory, maintaining balance during gait and accuracy of turning. The intelligent digital infrastructure included: Radiofrequency Identification (RFID) microcontroller with an Arduino Mega 2560 board and PC with OLED display. The development used RFID components: RFID tags located at key points of the L-test trajectory, RFID reader - which is located on the student and RFID - data processing system that accumulates and analyzes information, linking RFID elements into a single system. The signal received and processed by RFID when a student performs a test task is transmitted via radiofrequency communication to the Arduino Mega 2560 microcontroller board. The boarc provides the ability to process signals from RFID to calculate gait parameters when performing the L-test. To increase the efficiency of the intelligent digital infrastructure, Machine Learning algorithms and cloud data storage were implemented. Analysis of the results of the experimental study showed a «high» level of psychometric properties of the L-test for students with lower limb amputations implemented by the intelligent digital infrastructure in contrast to the results recorded by a stopwatch. Conclusions. The use of the intelligent digital infrastructure in the implementation of the L-test for students with lower limb amputations provides a high level of reliability and objectivity of the control results in real-time. The use of modern artificial intelligence technologies in the developed infrastructure allows analyzing large volumes of collected data and creating models capable of assessing the quality of test performance and identifying gait pathologies in students when performing the L-test.