Abstract

Robotic exoskeletons that induce leg movement have proven effective for lower body rehabilitation, but current solutions offer limited gait patterns, lack stabilization, and do not properly stimulate the proprioceptive and balance systems (since the patient remains in place). Partial body weight support (PBWS) systems unload part of the patient’s body weight during rehabilitation, improving the locomotive capabilities and minimizing the muscular effort. HYBRID is a complete system that combines a 6DoF lower body exoskeleton (H1) with a PBWS system (REMOVI) to produce a solution apt for clinical practice that offers improves on existing devices, moves with the patient, offers a gait cycle extracted from the kinematic analysis of healthy users, records the session data, and can easily transfer the patient from a wheelchair to standing position. This system was developed with input from therapists, and its response times have been measured to ensure it works swiftly and without a perceptible delay.

Highlights

  • Assisted gait training and rehabilitation have a high impact on healthcare and are characterized by scientific and technical challenges [1,2,3]

  • There is no consensus on which program is best suited for gait rehabilitation or which tools are most useful, especially for spinal cord injury (SCI) [4,5]

  • The HYBRID device is a gait trainer based on a double support system (Figure 1): a bilateral 3DoF

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Summary

Introduction

Assisted gait training and rehabilitation have a high impact on healthcare and are characterized by scientific and technical challenges [1,2,3]. Repetitive movement strategies have proliferated in clinical practice [6,7] They are designed to stimulate the central pattern generators (CPGs), responsible for generating coordinated movements [5,8]. Robotics-based rehabilitation offers a unique opportunity to solve this problem, improving training intensity and quality. By assisting both the therapists and the patients in Sensors 2019, 19, 4773; doi:10.3390/s19214773 www.mdpi.com/journal/sensors

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