Filtering Motion Capture Data for Real-Time Applications

In this paper we present some custom designed filters for real-time motion capture applications. Our target application is motion controllers, i.e. systems that interpret hand motion for musical interaction. In earlier research we found effective methods to design nearly optimal filters for realtime applications. However, to be able to design suitable filters for our target application, it is necessary to establish the typical frequency content of the motion capture data we want to filter. This will again allow us to determine a reasonable cutoff frequency for the filters. We have therefore conducted an experiment in which we recorded the hand motion of 20 subjects. The frequency spectra of these data together with a method similar to the residual analysis method were then used to determine reasonable cutoff frequencies. Based on this experiment, we propose three cutoff frequencies for different scenarios and filtering needs: 5, 10 and 15 Hz, which correspond to heavy, medium and light filtering, respectively. Finally, we propose a range of real-time filters applicable to motion controllers. In particular, low-pass filters and low-pass differentiators of degrees one and two, which in our experience are the most useful filters for our target application. Proceedings of the 13th International Conference on New Interfaces for Musical Expression. NIME’13, May 27 – 30, 2013, KAIST, Daejeon, Korea. Copyright remains with the author(s).