## 4 Discussion  
Despite dynamic development of measurement systems and techniques for the evaluation of the movement of human motor organs, assessment of upper limb motion dysfunctions is still dependent on medical scales and subjective tests. However, the use of indicators as a method of motion assessment has gained much interest in the research community over the last 20 years [14; 15; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. Indeed, the development of such indicators has been concurrent with technological advances in the equipment used for capturing kinematics. Ultimately, researchers are aiming to develop a universal indicator that could be used in the assessment of motion pathology for a range of diseases and disorders. Many studies have been conducted into the application of indicators for the evaluation of gait pathology, and some of the most popular indexes, such as GGI, GDI and GPS, have been verified by many research centres [24; 25; 26; 27]. Unfortunately, indicators for upper limb pathology have not gained the same level of attention, however there has been some attention on this issue from the research community. Indeed, the first upper limb indicator, the APS, was developed by [PERSON] et al. [15] in 2011. Since then, three additional indicators have been defined, namely, PULMI [17; 22; 23] GULDI and UMDI [18]. All of these upper body indicators were designed based on the same mathematical algorithm used for gait assessment, the GDI [24]. However, it is proposed that the mathematical algorithm of the GGI gait evaluation indicator [25], would be more suitable for development of an indicator for upper limb pathology. Nonetheless, the main challenge of using this algorithm is the appropriate selection of input parameters. With this in mind, this work aimed to develop methodology for optimal parameter selection in order to create a new UBI indicator for evaluation of upper limb motion pathology. Here, a complex approach to parameter selection was proposed, based on the concept that input parameters should include variables that best differentiate the results obtained in the stroke patient group from those obtained for the reference group. To achieve this, three selection methods were developed that took into account the percentage distribution of parameters beyond the standard (M1), the differences in mean values between the reference group and stroke patient group (M2), and the variability of parameters (M3). This approach revealed the diversity of particular parameters in the reference group and the group of patients with upper limb motor dysfunctions. In previous studies of indicator development, the methods used for parameter selection were not clearly defined and they were often proposed by the researchers or physicians with reference to a particular group of patients. Indeed, parameter selection for the development of the GGI indicator was tailored to a group of children with cerebral palsy [25; 26; 27; 28]. Though subsequent research has demonstrated that the GGI indicator could be used in the evaluation of gait in other patient groups [29]. Our approach consisted of a complex method of input parameter selection for the purposes of the development of a new indicator, through consultation with physicians and physiotherapists. Development of the new UBI indicator was achieved through identification of 2 temporal parameters and 14 kinematic parameters, which included minimum and maximum angles of the upper limb joints, motion ranges of the joints and spine movement (Table 9). These parameters assessed motion in the shoulder and elbow joints, in all possible planes, \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Dominant Limb** & **Non-Dominant Limb** \\ \hline mean \(\pm\) std & 130.86 \(\pm\) 75.07 & 155.58 \(\pm\) 170.76 \\ min–max & 45.85–351.03 & 30.17–630.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Data obtained for the Upper Body Index indicator in the stroke patient group. as well as movement of the spine.