Movements of a person can be recorded with a mobile camera and visualized as sequences of stick figures for assessments in health and elderly care, physio-therapy, and sports. However, since the visualizations flicker due to noisy input data, the visualizations themselves and even whole assessment applications are not trusted in general. The present paper evaluates different filters for smoothing the movement visualizations but keeping their validity for a visual physio-therapeutic assessment. It evaluates variants of moving average, high-pass, and Kalman filters with different parameters. Moreover, it presents a framework for the quantitative evaluation of smoothness and validity. As these two criteria are contradicting, the framework also allows to weight them differently and to automatically find the correspondingly best-fitting filter and its parameters. Different filters can be recommended for different weightings of smoothness and validity. The evaluation framework is applicable in more general contexts and with more filters than the three filters assessed. However, as a practical result of this work, a suitable filter for stick figure visualizations in a mobile application for assessing movement quality could be selected and used in a mobile app. The application is now more trustworthy and used by medical and sports experts, and end customers alike.