Abstract
Mobile robotic platforms for human gait analysis could open the gap to multiple medical applications and new discoveries. They could take several advantages over certified photogrammetric systems by making possible gait analysis without space limitations. In this document we present the design of a new ROS-based mobile robot platform for human gait analysis. All processes are ROS-based and Nuitrack SDK is used to develop the skeleton tracking application with a depth camera. During the procedure we described the design of the control law implemented for gait analysis. We developed a lead compensator by root locus method to increase the stability and speed response of the system. The error of measurement with respect to a certified photogrammetric system was considerably low during positioning task. Additional measurements were performed to verify the acquisition of gait parameters. These included spatio-temporal variables and range of movement (ROM) of knee and hip during joint excursions. Results showed that this mobile robotic platform represents a non-invasive alternative that could be improved for use in biomechanical human gait analysis.
Supported by Universidad Politécnica de Madrid.
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Acknowledgements
The research leading to these results has received funding from RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by “Programas de Actividades I+D en la Comunidad de Madrid” and co-financed by Structural Funds of the EU. The authors would like to thank Faculty of Physical Activity and Sports Sciences - INEF, UPM, for the use of Sports Biomechanics Laboratory. The authors also acknowledge Enrique Navarro Cabello and Javier Rueda for their contribution during the experimental stage.
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Guffanti, D., Brunete, A., Hernando Gutierrez, M. (2021). Human Gait Analysis Using Non-invasive Methods with a ROS-Based Mobile Robotic Platform. In: Rauter, G., Cattin, P.C., Zam, A., Riener, R., Carbone, G., Pisla, D. (eds) New Trends in Medical and Service Robotics. MESROB 2020. Mechanisms and Machine Science, vol 93. Springer, Cham. https://doi.org/10.1007/978-3-030-58104-6_35
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DOI: https://doi.org/10.1007/978-3-030-58104-6_35
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