Performance evaluation of gesture-based interaction between different age groups using Fitts' Law

The recent advances made in human-computer interaction have allowed us to manipulate digital contents exploiting recognition-based technologies. However, no work has been reported that evaluates how these interfaces influence the performance of different user groups. With the appearance of multiple sensors and controllers for hand gesture recognition, it becomes important to understand if these groups have similar performance levels concerning gestural interaction, and if some sensors could induce better results than others when dealing with users of different age brackets. In this respect, it could also be important to realize if the device's sensor accuracy in terms of hand / full body recognition influences interaction performance. We compare two gesture-sensing devices (Microsoft Kinect and Leap Motion) using Fitts' law to evaluate target acquisition performances, with relation to users' age differences. In this article, we present the results of an experiment implemented to compare the groups' performance using each of the devices and also realize which one could yield better results. 60 subjects took part in this study and they were asked to select 50 targets on the screen as quickly and accurately as possible using one of the devices. Overall, there was a statistically significant difference in terms of performance between the groups in the selection task. On the other hand, users' performance showed to be rather consistent when comparing both devices side by side in each group of users, which may imply that the device itself does not influence performance but actually the type of group does.