Article Details

Abstract

Aiming at the problems of poor adaptability and insufficient human-machine coordination in traditional gait generation methods for lower limb exoskeletons, this paper proposes a data-driven method based on inertial motion capture and multi-body dynamics simulation. The gait data of healthy individuals and patients are collected by the Noitom system. The joint angle trajectories are generated by using spline curve fitting, and a dynamic adaptation algorithm is designed to achieve real-time adjustment of gait planning. A human-machine joint simulation model is constructed in ADAMS, and the CUBSPL spline is used to drive joint movement for systematic kinematic and dynamic analysis. The results show that the generated exoskeleton gait is highly consistent with the natural gait in terms of joint angle, displacement, velocity, and torque, which verifies the effectiveness of the method. This study provides a complete technical path for personalized gait planning and rapid verification of lower limb exoskeletons, and has reference value for improving the adaptability and safety of rehabilitation training.

Keywords

lower limb exoskeleton; inertial motion capture; gait generation; ADAMS simulation; data-driven

References

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Funding

Guizhou University Open Experimental Fund (YSKF2024-81)

About the authors

Zhang Yuxuan (2005-), male, Han ethnicity, from Changtu, Liaoning Province, undergraduate student, research direction: mechanical design, manufacturing and automation, intelligent manufacturing; Lu Feng (1978-), male, Buyi ethnic group, from Guiyang, Guizhou, lecturer, research direction: manufacturing informatization, Internet of Things engineering.