Accurately parameterizing human gait is highly important in the continued development of assistive robotics, including but not limited to lower limb prostheses and exoskeletons. Previous studies introduce the idea of time-invariant real-time gait parameterization via human-inspired phase variables. The phase represents the location or percent of the gait cycle the user has progressed through. This thesis proposes an alternative approach for determining the gait phase leveraging previous methods and a canonical dynamical system.
Human subject experiments demonstrate the ability to accurately produce a phase variable corresponding to the human gait progression for various walking configurations. Configurations include changes in incline and speed. Results show an augmented real-time approach capable of adapting to different walking conditions
