Velocity-dependent reference trajectory generation for the LOPES gait training robot

An Adaptable Human-Like Gait Pattern Generator Derived From a Lower Limb Exoskeleton

Walking rehabilitation processes include many repetitions of the same physical movements in order to replicate, as close as possible, the normal gait trajectories, and kinematics of all leg joints. In these conventional therapies, the therapist′s ability to discover patient′s limitations—and gradually reduce them—is key to the success of the therapy. Lower-limb robotic exoskeletons have strong deficiencies in this respect as compared to an experienced therapist. Most of the currently available robotic solutions are not able to properly adapt their trajectories to the biomechanical limitations of the patient. With this in mind, much research and development is still required in order to improve artificial human-like walking patterns sufficiently for valuable clinical use. 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: walking with three different pre-determined step lengths 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. Prior literature suggests analyzing gait in phases. 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., Heel Strike, Toe Off, Pre-Swing, Initial Swing, Mid-Swing, and Terminal Swing, it is possible to generate a new calculated ankle trajectory with an arbitrary step length. The Leave-One-Out Cross Validation algorithm was used to estimate the fitting error of the calculated trajectory vs. 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.

Robotics in Lower-Limb Rehabilitation after Stroke

With the increase in the elderly, stroke has become a common disease, often leading to motor dysfunction and even permanent disability. Lower-limb rehabilitation robots can help patients to carry out reasonable and effective training to improve the motor function of paralyzed extremity. In this paper, the developments of lower-limb rehabilitation robots in the past decades are reviewed. Specifically, we provide a classification, a comparison, and a design overview of the driving modes, training paradigm, and control strategy of the lower-limb rehabilitation robots in the reviewed literature. A brief review on the gait detection technology of lower-limb rehabilitation robots is also presented. Finally, we discuss the future directions of the lower-limb rehabilitation robots.

Robotic gait rehabilitation and substitution devices in neurological disorders: where are we now?

Gait abnormalities following neurological disorders are often disabling, negatively affecting patients' quality of life. Therefore, regaining of walking is considered one of the primary objectives of the rehabilitation process. To overcome problems related to conventional physical therapy, in the last years there has been an intense technological development of robotic devices, and robotic rehabilitation has proved to play a major role in improving one's ability to walk. The robotic rehabilitation systems can be classified into stationary and overground walking systems, and several studies have demonstrated their usefulness in patients after severe acquired brain injury, spinal cord injury and other neurological diseases, including Parkinson's disease, multiple sclerosis and cerebral palsy. In this review, we want to highlight which are the most widely used devices today for gait neurological rehabilitation, focusing on their functioning, effectiveness and challenges. Novel and promising rehabilitation tools, including the use of virtual reality, are also discussed.