Using artificial intelligence algorithms to approximate data from inertial measurement unit sensors and strain gauges in basketball players

Abstract

BACKGROUND: The process of acquiring visual data from microelectromechanical sensors currently requires significant time and effort on the part of the clinician. The use of artificial intelligence algorithms to approximate data could potentially reduce the time required and increase the amount of work performed. AIM: The aim of this study is to approximate the data generated by sensors located in the shoe insole of basketball athletes and to compare the change in movement parameters of athletes when using CAD/CAM insoles. MATERIALS AND METHODS: Prior to the commencement of the study, permission was obtained from the local ethical committee of Sechenov University (protocol No. 19–23). The main cohort consisted of 39 athletes, comprising 21 men (53%) and 18 women (47%). The mean age of the athletes was 22.4 ± 7.54 years. The athletes were divided into three equal comparison groups according to the type of insoles they were wearing. Throughout the study period, all athletes remained healthy and free from injuries. The assessment of movement in space was conducted using a three-test system. This involved the use of microelectromechanical system sensors with an artificial intelligence algorithm, which facilitated the construction of visually clear and well-interpreted median lines (data approximation). RESULTS: For objective assessment of jumping characteristics, angular changes, velocity movements in space, and a comparison of all parameters on days 0 and 21, we developed and used our own software system, which was based on mathematical algorithmization and transformation formulas on specific axes. All data were entered into a neural network to construct averaged values of the parameters of movement in space. This approach allows the doctor to evaluate the changes of each peak movement on three different axes. Furthermore, it is possible to summarize the athlete's movement parameters with the aid of artificial intelligence, thereby enabling the detection of changes in different axes on days 0 and 21. Insole model C-1 exhibited the following improvements: X-axis movement speed (+7.7%), Y-axis jump height (+17.3%), endurance (+3.1%), and a 1.43-fold enhancement in shock absorption. Insole model C-2 exhibited an 8.4% increase in X-axis travel speed, a 20.8% enhancement in Y-axis jump height, a 6.6% improvement in endurance, and a 1.48-fold enhancement in shock absorption. Insole model C-3 demonstrated an 13.5% surge in X-axis travel speed, a 22.4% surge in Y-axis jump height, a 9.5% surge in endurance, and a 1.53-fold enhancement in shock absorption. CONCLUSIONS: The approximation of the data (median lines using an artificial intelligence algorithm) allows for the straightforward interpretation and comparison of various parameters, as well as the drawing of conclusions regarding the efficacy of individual sports CAD/CAM insoles. Additionally, it enables the assessment of changes in endurance, speed of movement during prolonged and intensive movement, and the reduction of the risk of impact loads on the musculoskeletal system of the athlete.