B. Jansen, L. Omelina, B. Bonnechère, V. Sholukha and S. Van Sint Jan in Proceedings of the 14th Belgian National Day on Biomedical Engineering
This abstract presents a novel method developed to evaluate shoulder motions of patients performing rehabilitation exercises. 1. Background
The shoulder complex is one of the most movable joints of the body. Clinicians do not have tools to assess three-dimensional (3D) shoulder range of motion (RoM) during complex motions. Currently, most clinical motion analysis centers use a marker-based system (MBS). Although the MBS validity is high with respect to the positions of the markers in 3D space, some problems occur with MBS in daily practice: accuracy and mainly reproducibility of such a system is still controversial for the estimation of joint centers and relative segment orientations. Another potential problem of the motion analysis is that the patients are not in a familiar environment (in underwear with markers glued on the body, performing some predefined motions). The Kinect™ sensor, a cost-effective markerless motion capture system (MLS), offers interesting possibilities in clinical functional analysis and rehabilitation. However, to our best knowledge, no study has investigated the use of a Kinect™ sensor to analytically evaluate 3D motion of the shoulder complex during complex motions. This device was originally developed for gaming purpose 2. Methodology
A rehabilitation platform was developed using the Kinect to detect and track motions performed by the patients during the games. The advantage of this system is that when patients are immersed in the game (e.g. virtual reality) they are performing more natural motion because they are focusing on the games and pay less attention to the pain or other kind of limitation. Therefore, the aim of this study was to assess the capacities of this system and to determine its limitations in order to understand whether or not it can be used in daily clinics to assess functional shoulder motions through specially developed physical rehabilitation exercises. Twenty subjects participated in this study three functional motions were simultaneously recorded with a Marker Based System (MBS) and MLS. Correlations, limits of agreements, standard error of measurement (SEM) and minimal detectable changes (MDC) were processed 3. Results and Discussion
In clinics, the reproducibility of a measurement is a key element to determine whether or not a device can be used to perform patients’ assessment and follow up. Systematic and reproducible differences were found (ICC=0.80 for MLS, ICC=0.72 for MBS). Concerning SEM and MDC better results were found for MLS compared to MBS. In summary, these results indicate that there are differences between the two devices, but these differences are systematic and reproducible. Results are correlated with MBS (after optimization). Our results indicate that errors from the joint center estimation using MLS are less important than errors induced by markers’ placement and displacement during complex motion. Therefore, we found better results for SEM and MDC compared to MBS. 3D motion analysis of the upper limb using MLS coupled to a post-processing optimization procedure can thus be achieved with the Kinect™ sensor. This procedure also allows 3D motions representations based on the trajectory for the clinicians to perform regular follow-up of the patient.