Neglected physical human-robot interaction may explain variable outcomes in gait neurorehabilitation research.

During gait neurorehabilitation, many factors influence the quality of gait patterns, particularly the chosen body-weight support (BWS) device. Consequently, robotic BWS devices play a key role in gait rehabilitation of people with neurological disorders. The device transparency, support force vector direction, and attachment to the harness vary widely across existing robotic BWS devices, but the influence of these factors on the production of gait remains unknown. Because this information is key to designing an optimal BWS, we systematically studied these determinants in this work. We report that with a highly transparent device and a conventional harness, healthy participants select a small backward force when asked for optimal BWS conditions. This unexpected finding challenges the view that during human-robot interactions, humans predominantly optimize energy efficiency. Instead, they might seek to increase their feeling of stability and safety. We also demonstrate that the location of the attachment points on the harness strongly affects gait patterns, yet harness attachment is hardly reported in literature. Our results establish principles for the design of BWS devices and personalization of BWS settings for gait neurorehabilitation.

Preserved gait kinematics during controlled body unloading.

BACKGROUND: Body weight supported locomotor training was shown to improve walking function in neurological patients and is often performed on a treadmill. However, walking on a treadmill does not mimic natural walking for several reasons: absent self-initiation, less active retraction of leg required and altered afferent input. The superiority of overground training has been suggested in humans and was shown in rats demonstrating greater plasticity especially in descending pathways compared to treadmill training. We therefore developed a body weight support system allowing unrestricted overground walking with minimal interfering forces to train neurological patients. The present study investigated the influence of different amounts of body weight support on gait in healthy individuals. METHODS: Kinematic and electromyographic data of 19 healthy individuals were recorded during overground walking at different levels of body weight support (0, 10, 20, 30, 40, and 50%). Upper body inclination, lower body joint angles and multi-joint coordination as well as time-distance parameters were calculated. Continuous data were analyzed with regard to distinct changes within a gait cycle across all unloading conditions. RESULTS: Temporal gait parameters were most sensitive to changes in body unloading while spatial variables (step length, joint angles) showed modest responses when unloaded by as much as 50% body weight. The activation of the gastrocnemius muscle showed a gradual decrease with increasing unloading while the biceps femoris muscle showed increased activity levels at 50% unloading. These changes occurred during stance phase while swing phase activity remained unaltered. CONCLUSIONS: Healthy individuals were able to keep their walking kinematics strikingly constant even when unloaded by half of their body weight, suggesting that the weight support system permits a physiological gait pattern. However, maintaining a given walking speed using close-to-normal kinematics while being unloaded was achieved by adapting muscle activity patterns. Interestingly, the required propulsion to maintain speed was not achieved by means of increased gastrocnemius activity at push-off, but rather through elevated biceps femoris activity while retracting the leg during stance phase. It remains to be investigated to what extent neurological patients with gait disorders are able to adapt their gait pattern in response to body unloading.

Multidirectional transparent support for overground gait training.

Gait and balance training is an essential ingredient for locomotor rehabilitation of patients with neurological impairments. Robotic overhead support systems may help these patients train, for example by relieving them of part of their body weight. However, there are only very few systems that provide support during overground gait, and these suffer from limited degrees of freedom and/or undesired interaction forces due to uncompensated robot dynamics, namely inertia. Here, we suggest a novel mechanical concept that is based on cable robot technology and that allows three-dimensional gait training while reducing apparent robot dynamics to a minimum. The solution does not suffer from the conventional drawback of cable robots, which is a limited workspace. Instead, displaceable deflection units follow the human subject above a large walking area. These deflection units are not actuated, instead they are implicitly displaced by means of the forces in the cables they deflect. This leads to an underactuated design, because the deflection units cannot be moved arbitrarily. However, the design still allows accurate control of a three-dimensional force vector acting on a human subject during gait. We describe the mechanical concept, the control concept, and we show first experimental results obtained with the device, including the force control performance during robot-supported overground gait of five human subjects without motor impairments.