Artificial intelligence method to detect psychological/ learning disorders in physical education and sports activities

Abstract

Objective of the study was to analyze benefits of an artificial intelligence application method for detecting psychological/
learning disorders in the physical education and sports sector.
Methods and structure of the study. Many Russian scientists including A.I. Akhmetzyanov, S.A. Vasyur and N.I. Iogolevich, A.N. Gud et al. have made efforts to adapt the existing intellectual analytical methods and develop new ones using temporal series to describe poorly structured processes and detect disorders thereof; although, regretfully, modern artificial intelligence methods are still underdeveloped in application to the physical education and sports service abnormalities
detection purposes. Basically, a disorder detection approach will be designed to determine whether some process (or local data array) falls into the normality field – and if not, rate it abnormal.
Abnormalities in data arrays are usually suspected in cases of omissions or excesses in the data groups going beyond the permissible range, whilst the disorder detection approaches should address, in addition to the above, the behavioral anomalies in the entire data range including specific disorders in the local trends. Therefore, specific detection and analyzing
methods need to be selected as dictated by some of the two above provisions.
Results and conclusion. Special selected methods and algorithms applied by artificial intelligence systems help not only successfully find disorders/ abnormalities in the data arrays – where genuine correlations of specific indicators can hardly be found by other means, particularly when the indicators refer to complex psychological, learning and/ or psycho-physiological phenomenon/ process – but also effectively forecast consequences of the detected disorders.