Sonication in Kinesiophobia Therapy: Presenting Motion Capture Data as Mechanical Quantities

Abstract: Kinesiophobia is a severe limitation in its victims' lives, this paper is part of a larger study aiming to use motion capture combined with a reliable mechanical model to provide auditory feedback on a kinesiophobia victim's locomotion on a track designed to inspire movements relevant for rehabilitation. This paper focuses on creating a theoretical model with a foundation in the biomechanics eld and uses it to establish programming logic that facilitates sonication methods used on the recorded motion data. Motion capture is a widely used technique and a lot of research has been done on how to use it, this paper brings up examples of skeleton gen-eration and movement categorization. Nonetheless, there is no precedent using quantities entirely based on rigid body coordinates for the sonication of motion data as a possible segment in kinesiophobia therapy. The mechanical quantities that were analyzed in this study are based solely on travelled distances and their derivatives to minimize the customization need for dierent subjects. Functions calculating the quantities of interest based on a motion capture stream were written in MatLab and programatically run from a script in Java, where the data was stored. A swift tool for analyzing valuable motion quantities in real time was developed and tested successfully, the full procedure is disclosed along with suggested improvements and an evaluation of its strengths and drawbacks.