Kinematic and Dynamic Analysis of Lower Limb Movement: Towards the Design of a Wearable Rehabilitation Assistant Device

Main Article Content

Filippos Margaritis
Konstantinos Mitsopoulos
Kostas Nizamis
Alkinoos Athanasiou
Panagiotis D. Bamidis

Keywords

Lower Limb Kinematic Analysis, Lower Limb Dynamic Analysis, OpenSim, OpenSenseRT System, IMU Inverse Kinematics, Real-Time Inverse Kinematics, Real-Time Motion Analysis, Wearable Rehabilitation Assistant Device

Abstract

This study outlines a comprehensive approach to the kinematic and dynamic analysis of lower limb movement, with the express purpose of designing an efficient wearable rehabilitation assistant device for the lower body. The approach begins by conducting a kinematic analysis of the lower limbs, presenting the degrees of freedom and each joint’s range of motion. A kinematic model is designed by deciding on a kinematic chain configuration and calculating the Denavit Hartenberg (DH) parameters. Next, differential kinematic analysis is employed to calculate the velocity of the limbs, generated by the corresponding muscle groups during different types of movements. This can provide significant insights into the design of a device that can accurately track and assist these movements. Furthermore, a dynamic analysis is performed to calculate joint moments and forces. This analysis provides insights into the forces that the joints experience during movement. When combined with electromyography (EMG) data, it allows for a more holistic description of muscle activity and a more accurate estimation of individual muscle forces and joint loads. The research also lays out a plan for the wearable device's implementation. Based on OpenSenseRT [1] an open-source software and hardware project, that utilized the OpenSim [2] API, real-time inverse kinematics of a movement can be calculated using data from inertial measurement units (IMUs). This data is then used to compute the error in a person's movement during lower limb rehabilitation exercises. This error, along with the error derived from real-time dynamic analysis and EMG data, can be integrated to improve the control accuracy of the wearable device.

Downloads

Download data is not yet available.
Abstract 224 | PDF Downloads 50

References

1. Slade, P., Ayman, H., Jennifer, L.H., et al. An open-source and wearable system for measuring 3D hu-man motion in real-time. IEEE Trans Biomed Eng. 2022;69(2):678–688. https://doi.org/10.1109/TBME.2021.3103201.
2. Seth, A., Hicks, J.L., Uchida, T.K., et al. OpenSim: Simu-lating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLoS Comput Biol. 2018;14(7):e1006223. https://doi.org/10.1371/journal.pcbi.1006223.
3. Ziegler, J., Reiter, A., Gattringer, H., et al. Simultaneous identification of human body model parameters and gait trajectory from 3D motion capture data. Med Eng Phys. 2020;84:193–202. https://doi.org/10.1016/j.medengphy.2020.08.009.
4. Range of Joint Motion Evaluation Chart. Washington State Department of Social and Health Services (2014). Available online: https://www.dshs.wa.gov/sites/default/files/forms/pdf/13-585a.pdf.
5. Hamilton, N., Weimar, W., Luttgens, K. Kinesiology: Sci-entific Basis of Human Motion (B&B PHYSICAL EDUCA-TION), McGraw-Hill Education: New York, USA; 2011.
6. Craig, J.J. Introduction to robotics: mechanics and control. Addison-Wesley Publishing Company; 2005; pp.303.
7. Spong, M.W., Hutchinson, S., Vidyasagar, M. Robot mod-eling and control, 2nd ed. John Wiley & Sons: Hoboken, NJ, USA; 2020; pp. 107–205.
8. Baluch, T.H., Masood, A., Iqbal, J., et al. Kinematic And Dynamic Analysis Of A Lower Limb Exoskeleton. IJMME.2012;6(9):1945–1949. https://doi.org/10.5281/zenodo.1072880.
9. Siciliano, B., Sciavicco, L., Villani, L., et al. Robotics: modelling, planning and control. Springer Science & Business Media: Berlin, Germany; 2010.
10. Pizzolato, C., Lloyd, D.G., Sartori, M., et al. CEINMS: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks. J Biomech. 2015;48(14):3929–3936. https://doi.org/10.1016/j.jbiomech.2015.09.021.