# Test-Retest Repeatability of Human Gestures in Manipulation Tasks
## 4 Discussion  
Since abrupt movements are randomly caused by involuntary reactions, their accelerations are greater than those of normal movements, as shown by the comparison between the green and the other two clusters presented in Figure 3. Moreover, abrupt movements generated by the acoustic alarm (blue cluster) produce greater accelerations than abrupt movements generated by the visual alarm (red cluster). This trend can be explained considering the specific protocol of this study, because the movement required in response of an acoustic alarm was wider than the one associated with a visual alarm. Overall, the distribution of the three groups follows the principal diagonal, suggesting a repeatability between test and retest RMS values (Figure 3). The definition of a band based on the interquartile range of RMS values allowed the identification of a number of outliers between 0 and 3 for each combination of the three types of movements and the three modalities (Figure 4). As reported in Tables 1-3, the removal of outliers (no_o) before the evaluation of test-retest repeatability did not produce a significant improvement with respect to the analysis involving all participants (all). On average, it is possible to note a minimum increase in both ICC (3,1) (normal +0.07, abrupt_visual +0.10, abrupt_acoustic +0.13) and ICC (1,1) (normal +0.07, abrupt_visual +0.10, abrupt_acoustic +0.11). Coherently, CV values exhibit a slight reduction (normal \(-0.75\%\), abrupt_visual \(-1.56\%\), abrupt_acoustic \(-2.08\%\)). In industrial scenarios, real-time should be a key feature for performance optimization. Indeed, an online risk assessment is suitable to evaluate the biomechanical load in repetitive efforts [24] or manual material handling [25] to build a warning system for the prevention of musculoskeletal disorders [26] and to improve assembly workstations [27]. Considering the scenarios of human-machine interaction, a real-time exchange of information between the operator and the robot improves both the interaction [28; 29] and the robotic control [30; 31]. Accordingly, the similarity of repeatability with and without the outliers (all vs. no_o) represents a positive aspect to approach real-time conditions. Indeed, the possibility of avoiding the post-elaboration of data guarantees the reduction in time. Another important aspect highlighted from the results is the influence of the time occurring between test and retest sessions. Indeed, when this time was \(<\) 45 days (u45), the test-retest repeatability was higher with respect to the analysis related to a test-retest time \(\geq\) 45 days (o45). On average, a general strong increase can be noticed in both ICC (3,1) (normal +0.67, abrupt_visual +0.13, abrupt_acoustic +0.35) and ICC (1,1) (normal +0.68, abrupt_visual +0.14, abrupt_acoustic +0.35). Coherently, an evident reduction involved CV values (normal \(-8.03\%\), abrupt_visual \(-4.06\%\)). The only exception is represented by abrupt movements generated by the acoustic alarm with an average increment of CV (+2.27%) from u45 to o45 despite a strong increase in both ICCs. In industrial scenarios, deep learning techniques can be involved to provide the robot with important information about the human motion in real-time. Consequently, these results suggest updating the network training within around 45 days. Indeed, as reported in the literature [19], repeatability can be considered poor if ICC \(<\) 0.4, fair-to-good if 0.4 \(\leq\) ICC \(<\) 0.75 and excellent if ICC \(\geq\) 0.75. Observing results related to u45 condition, average values of ICCs are 0.78 for normal movements, 0.48 for abrupt movements generated by the visual alarm and 0.68 for abrupt movements generated by the acoustic alarm. As expected, normal movements are characterized by an excellent repeatability because they are repetitive and predictable. Accordingly, when abrupt gestures occur, the involved higher accelerations cause lesser controlled movements and hence produce lower ICC values.