We show that the Kinematic Theory produces synthesized stroke gestures that ‘look and feel’ the same and hold the same statistical characteristics as human-generated gestures. Previous research in this vein has conducted such comparison from the classification accuracy performance, which is a legitimate though indirect measure. In this article, we synthesized two well-known public data sets comprising unistroke and multistroke gestures. We then compared geometric, kinematic and articulation aspects of human and synthetic gestures, and found no practical differences between both populations. We also conducted an online survey involving 236 participants and found that it is very difficult to tell human and synthetic gestures apart. We can finally be confident that synthesized gestures are actually reflective of how users produce stroke gestures. In sum, this work enables a deeper understanding of synthetic gestures’ production, which can inform the design of better gesture sets and development of more accurate recognizers.