Converting data from motion analysis systems based on inertial and magnetic sensors to motion analysis systems based on stereophotogrammetry - Di Tore, A.; Vastola, R.; Prosperi, R.; Raiola, G.; Sibilio, M. (17th Annual Congress of the European College of Sport Science (ECSS), Bruges, 4. -7. July 2012, 2012)
Kinematic motion analysis techniques can vary by their input methods; there are two primary input methods: magnetic systems based on inertial and magnetic sensors, and optical systems based on stereophotogrammetry The use of instrumentation based on stereophotogrammetry requires the measurement to be carried out in a laboratory This implies problems relative to dimension of a laboratory and to measuring in artificial environment The Inertial and Magnetic Measurement Systems (IMMSs) allow the user to execute and acquire the movement in laboratory-free settings (Cutti, Ferrari et al. 2010), but actually there is a lack of protocols to process the data acquired by IMMs. Furthermore, methodological differences make it difficult to compare results obtained with different protocols, and a standardization is difficult to implement This pilot work is part of a global project that aims at determining the accuracy of a motion inertial measurement system. Methods: In order to compare data acquired via IMMS with data acquired via an optical video based system, and to process them according to same protocol, the work describes the development of a software module able to create an interchange data format, converting the joint kinematics data from Xsens IMMs Moven to a format compatible with Smart Analyzer Software. Results: The software parses the non-well-formed MVNX, an Xml file format of Xsens, calculate the position of additional points not considered in MVNX file and required by chosen protocol, and convert the data in EMT, a file format used by BTS bioengineering Smart Analyzer Software. The MVNX (Moven Open XML format) files contains 3D position and 3D orientation of each segment (not the sensors) in XML format (Roetenberg, Luinge et al. 2009). The section defines all positions of connecting joints and anatomical landmarks with respect to origin of that segment, and the section contains the actual motion capture data. Each time frame consists of one row, containing 23 segments with 7 channels per segment (4 quaternion, 3 position) = 161 columns of data. The output EMT ASCII format contains only frame-per-frame 3D position of points (and not segments). Conclusions: The software is released under GNU/GPL license and a web-based version is available at Unisa Laboratorio H website.