## 4 Discussion  
Experimental protocols for EMG signal measurements are widely applied for an in-depth understanding of biomechanical problems. They are also implemented in rehabilitation, sports, medical, and scientific research, as well as in robotics and devices for controlling human limbs, etc. From the experiment to the real implementation of the results, there is a long way to go, using many studies and large databases. Furthermore, when we \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ICA Detected PC** & **ICA Detected PC** & **Detected Concance** & **Tasks for Further** \\
**between** & **between** & **Detection Phases** & **Consideration** \\ \hline
10 fsp-6 fsp & 10 esp-6 esp & strong PC, PC & TASK 3-TASK 4 \\
10 fsp-2 fspW & & & \\
1 fsp-6 fspW & & & \\
10 fspW-6 tspW & 10 pspW-6 espW & strong PC, PC & TASK LOAD 7-TASK \\ LOAD 8 \\
10 fspW-2 espW & & & \\
6 fspW-1 spsW & & & \\
2 fspW-1 spsW & 2 espW-1 spsW & strong PC, PC, weak PC & TASK LOAD 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between flexion- and extension-phase pairs in positive consonance (PC). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Extension Phases** & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** & **S8**  
discuss human research, human treatment and absolute safety insurance are mandatory, according to the Declaration of Helsinki [31]. Hence, painless and less exhausting studies are demanded for biomedical EPs. Decision-making in biomedicine is a complicated problem that requires compromises between different conflicting goals. For instance, the choice between a longer and more exhausting but informative EP and a shorter and easier EP that could be performed by a larger group of volunteers and patients with motor deficits must be made. The recently developed lCrA approach, as a decision making-method, can give the answer. Therefore, lCrA is applied to optimize the number of tasks included in the FEP for surface EMG signal measurements. The optimization of the full experimental protocol follows seven steps, given below:  
1. Preliminary preparation for EMG data measurements: Determination of the purpose of the research; selection of surface muscles for EMG signals recording; determination of the type and sequence of movements in the FEP; determination of inclusion and exclusion criteria for the participants. 2. Selecting suitable subjects for the study. 3. FEP execution, including training on and performing the cyclic movements. 4. EMG data processing, including the choice of the best cyclic movement (BCM). BCM separation into four phases; application of filters; signal rectification; normalization and calculation of area under the obtained curve. 5. lCrA application for optimization of the FEP, based on the calculated values for the flexion and extension phases. 6. Objective (based on lCrA) and subjective (based on the researcher's opinion) assessment of the results. 7. Obtaining the OEP. First, the lCrA correlations between eight flexion phases (presented in Table 2) from the considered cyclic movements (TASK 3 to TASK LOAD 10) were calculated. Next, lCrA was applied to estimate the relations between eight extension phases (shown in Table 3) from the same movements (TASK 3 to TASK LOAD 10). The purpose was to determine the tasks that could be excluded from the FEP. We seek the correlations in positive consonance simultaneously appearing for both the flexion and extension phases from the same task. The following correlations are found: 10 fsp-6 fsp and 10 esp-6 esp (from TASK 3 and TASK 4), 10 fspW-6 fspW and 10 espW-6 espW (from TASK LOAD 7 and TASK LOAD 8), and 2 fspW-1 fspW and 2 espW-1 espW (from TASK LOAD 9 and TASK LOAD 10). In the next stage, we decide which phase included in the correlated pair should remain and which to drop. We can easily see that the 10 fsp/10 esp phases are more time-consuming (10 s) than 6 fsp/6 esp (only 6 s). Here, the longer execution time is an exclusion criterion.