## 1 Introduction  
InterCriteria analysis (ICrA) was elaborated by [PERSON] et al. [1] in 2014. Intended as a solution to a specific production problem, it provoked unabated interest. ICrA establishes the correlation dependencies between chosen criteria and thus allows more time-consuming or more expensive measuring criteria to be removed from the evaluation process. As a result, the production costs are reduced. ICrA has numerous applications in different fields. Here, we will mention those related to biomedicine and quality of life due to the specific area of the current investigation. Several scientific collectives have conducted studies on health indicators for healthy people and patients with specific diseases, such as [PERSON]'s disease, cardiac conditions, diabetes, ear inflammation, respiratory diseases, gastritis, and asthma. In [PERSON] et al. [2], ICrA was applied to establish the correlations between health-related indicators in a questionnaire survey among 1050 respondents. This research aimed to boost people's health control and thus improve it. [PERSON] et al. [3; 4; 5] also use health indicators to discover ICrA dependencies in an experimental dataset of patients with [PERSON]'s disease. In addition, the decision-making approach helps to determine the appropriate treatment courses such as medicine, physiotherapeutic, and kinesitherapeutic programs. In the other four articles, ICrA was used as follows: to estimate the correlations between body composition indices associated with overweight in healthy students [6]; to support the process for criteria pre-selection for a large set of 88 ECG criteria [7] concerning patients with arrhythmia; to compare physiological rhythm curves [8]; and to search for relationships between the level of air pollution and the number of people with a variety of health problems seeking medical help [9]. ICrA was proven to be a promising tool for data analysis in several investigations on cancer diseases. [PERSON] et al. [10] apply this approach, along with correlation analysis, to define the interrelations between calorimetric parameters and statistical ones, acquired from blood plasma proteome thermograms of patients suffering from colorectal cancer. The authors show that both approaches complement each other and can help with deep analysis of calorimetric datasets. In [PERSON] et al. [11], the authors test the same decision-making method along, with two kinds of correlation analysis (Pearson's and Spearman's), for a large dataset. They use calorimetric and biochemical parameters, obtained from the serum proteome of multiple myeloma patients. [PERSON] et al. [12] use real data from prostate carcinoma patients to verify two new InterCriteria analysis modifications. The classical decision-making method supports four papers [13; 14; 15; 16] investigating blood collection at the Centers for Transfusion Haematology in Bulgaria. Valuable results were obtained for correlations between center activities, the age and sex dependencies of the donors, transfusion hematology processes over the years, and also different blood type distributions across the population in the country. The ICrA tool was applied in a kinesiological study for the first time in [17], focusing on a novel strategy for finding muscle interactions. The authors examined the repeatability and similarity of muscle synergies in healthy subjects using variable elbow motion velocities and applied or non-applied additional weight on the wrist. Biomedical research such as that mentioned above is associated with certain obstacles. The study of human biomechanics represents a serious challenge in understanding and reproducing movement principles. Motor task protocol construction for functional assessment or movement pattern learning is not an easy initiative. Its implementation is sometimes complicated. An in-depth knowledge of normal human kinetics can aid in understanding the mechanisms of abnormal conditions as a result of disease and injury.