An Adaptable Human-Like Gait Pattern Generator Derived From a Lower Limb Exoskeleton - Mendoza-Crespo, Rafael; Torricelli, Diego; Huegel, Joel Carlos; Gordillo, Jose Luis; Pons, Jose Luis; Soto, Rogelio (Frontiers in Robotics and AI, 2019)
Walking rehabilitation processes include many repetitions of the same physical movements in order to replicate, as close as possible. Most of the currently available robotic solutions are not able to properly adapt their trajectories to the biomechanical limitations of the patient. The work herein reported develops and presents a method to acquire and saliently analyze subject-speciﬁc gait data while the subject dons a passive lower-limb exoskeleton. Furthermore, the method can generate adjustable, yet subject-speciﬁc, kinematic gait trajectories useful in programming controllers for future robotic rehabilitation protocols. A human-user study with ten healthy subjects provides the experimental setup to validate the proposed method. The experimental protocol consists in capturing kinematic data while subjects walk, with the donned H2 lower-limb exoskeleton, across three experimental conditions marked on a lane. The captured ankle trajectories in the sagittal plane were found by normalizing all trials of each test from one heel-strike to the next heel-strike independent of the speciﬁc gait features of each individual. A preliminary data analysis, however, suggests that there exist six key events of the gait cycle, events that can adequately characterize gait for the purposes required of robotic rehabilitation including gait analysis and reference trajectory generation. Deﬁning the ankle as an end effector and the hip as the origin of the coordinate frame and basing the linear regression calculations only on the six key events, i.e. HS, TO, PS, IS, MS and TS, it is possible to generate a new calculated ankle trajectory with an arbitrary step length. The LOOCV algorithm was used to estimate the ﬁtting error of the calculated trajectory versus the characteristic captured trajectory per subject, showing a ﬁdelity average value of 95.2%, 96.1% and 97.2% respectively for each step-length trial including all subjects. This research presents method to capture ankle trajectories from subjects and generate human-like ankle trajectories that could be scaled and computed-on-line, could be adjusted to different gait scenarios, and could be used not only to generate reference trajectories for gait controllers, but also as an accurate and salient benchmark to test the human likeness of gait trajectories employed by existing robotic exoskeletal devices.