Modeling the Human Gait Phases by Using Bèzier Curves to Generate Walking Trajectories for Lower-Limb Exoskeletons.

The clinical usage of powered exoskeletons for the rehabilitation of patients affected by lower limb disorders has been constantly growing in the last decade. This paper presents a versatile and reliable gait pattern generator for these devices able to accommodate several gait requirements, i.e., step length, clearance, and time, and to suit a wide range of persons. In the proposed method, the human gait phases have been modeled with a set of trajectories as Bèzier curves, enabling a robotic lower-limb exoskeleton to walk in a continuous way, similarly to the physiological gait cycle. The kinematic, kinetic, and spatial requirements for each gait phase are translated into the control points of the Bèzier curves that define the trajectory for that phase. The outcome of this study has been tested on real scenarios with a group of healthy subjects wearing the TWIN lower-limb exoskeleton. They were asked to walk at different speeds, generally defined as slow, medium, and fast. The results are shown in terms of joint positions, velocities, and body-mass-normalized torques. The maximum hip and knee joint torque was observed in the support phase. While, at higher speeds the maximum hip torque was provided in the swing phase due to the mechanical properties and limits of the device. In terms of speed, all the subjects reached 0.44 m/s, which is the minimum required community ambulation.

Exploiting Natural Locomotion Synergies to Incorporate Motion of Crutches for Adaptive Gait Pattern Shaping.

This paper addresses the problem of online and adaptive gait pattern generation for powered lower-limb exoskeletons (PLLEs), exploiting the motion of sensorized crutches. We conduct a series of experiments with subjects walking with and without crutches to investigate the synergies of walking between upper and lower body segments, by adopting principal component analysis (PCA), We also evaluate the effect of using crutches on the walking synergies, and we demonstrate that upper and lower limb walking synergies undergo slight changes in that case. However, the upper and lower limb synergies remain evident and can be exploited in order to use the motion of crutches as an input to PLLEs to identify a desired motion of the lower limb. We propose a method to use the results of synergy analysis to shape gait parameters in the real-time control of PLLEs. To evaluate the scalability of our approach for real-world applications, we conduct a number of experiments with subjects wearing a PLLE and using sensorized crutches to adaptively change the gait parameters of walking steps, depending on upper body actions.