A bio-inspired robotic test bench for repeatable and safe testing of rehabilitation robots

The development of new algorithms for controlling rehabilitation robots requires iterative testing prior experimentation with humans. Experiments in humans - especially in humans with physical impairments - pose several challenges regarding safety and repeatability of the testing conditions. To address this problem we propose the use of a test bench that uses a bio-inspired model of a human leg implemented on the leg orthosis of a robotic gait trainer. The model consists of a feedback controller, used to simulate muscle-tendon visco-elastic properties and spinal reflexes, and a feedforward stage simulating motor commands from higher brain centers. Abnormal limb neuro-mechanics, such as weakness or spastic-like behavior can then be simulated and tested against newly developed robotic algorithms. In this study, such bio-inspired robotic test bench was used to evaluate the performance of an algorithm for the assessment of the walking function (RAGA, Robot-Aided Gait Assessment). We hypothesized that the RAGA software is able to identify the level of simulated impairment and to localize in which phase of the gait cycle the impairment is more evident. Therefore, we simulated different levels and types of impairments at three walking speeds and evaluated the outcome measures of the RAGA algorithm. We could confirm that the RAGA was able to identify different levels of simulated impairment correctly and to provide useful insights into gait dynamics. Moreover, we determined how increasing walking speeds can cause a positive offset in the outcome measures. We believe that this test bench represents a very useful and versatile tool that can be applied for testing novel training and assessment strategies implemented in rehabilitation robots.