Vision-Based Marker-Less Spatiotemporal Gait Analysis by Using a Mobile Platform: Preliminary Validation

Benjamin Filtjens, Robin Amsters, Ali Bin Junaid, Nick Damen, Jeroen Van de Laer, Benedicte Vanwanseele, Bart Vanrumste, Peter Slaets

Abstract: Gait analysis is one of the most useful tools for assessing age-related conditions. This study describes the preliminary validation of a novel vision-based method for unobtrusive, ambulatory monitoring of spatiotemporal gait parameters. The method uses a mobile platform that is equipped with a Microsoft Kinect. A proprietary, generative tracker is used for measuring the 3D segmental movement of the subject. A novel method was developed for extracting gait parameters from the raw joint measurements by using the relative distance between the two ankle joints. The results are assessed in terms of mean absolute error and mean absolute percentage error with respect to a motion capture system. The mean absolute error ± precision was 5.5 ± 3.5 cm for stride length, 1.7 ± 1.3 cm for step width, 0.93 ± 0.44 steps/min for cadence, and 2.5 ± 2.0% for single limb support. While these results are promising, additional experiments are required to assess the repeatability of this approach.