Due to the complexity of the human musculoskeletal system and intra/intersubjects variability, powered exoskeletons are prone to human-robot misalignments. These induce undesired interaction forces that may jeopardize safe operation. Uncompensated inertia of the robotic links also generates spurious interaction forces. Current design approaches to compensate for misalignments rely on the use of auxiliary passive degrees of freedom that unavoidably increase robot inertia, which potentially affects their effectiveness in reducing undesired interaction forces. Assessing the relative impact of misalignment and robot inertia on the wearer can, therefore, provide useful insights on how to improve the effectiveness of such approaches, especially in those situations where the dynamics of the movement are quasi-periodic and, therefore, predictable such as in gait. In this paper, we studied the effects of knee joint misalignments on the wearer's gait, by using a treadmillbased exoskeleton developed by our group, the ALEX II. Knee joint misalignments were purposely introduced by adjusting the mismatch between the length of the robot thigh and that of the human thigh. The amount of robot inertia reflected to the user was adjusted through control. Results evidenced that knee misalignment significantly changes human-robot interaction forces, especially at the thigh interface, and this effect can be attenuated by actively compensating for robot inertia. Misalignments caused by an excessively long robot thigh are less critical than misalignments of equal magnitude deriving from an excessively short robot thigh.Index Terms-Active leg exoskeleton (ALEX II), force control, human-robot misalignment, rehabilitation robotics.

In this paper, we discuss robot-mediated neurorehabilitation as a significant emerging field in clinical medicine. Stroke rehabilitation is advancing toward more integrated processes, using robotics to facilitate this integration. Rehabilitation approaches have tremendous value in reducing long-term impairments in stroke patients during hospitalization and after discharge, of which robotic systems are a new modality that can provide more effective rehabilitation. The function of robotics in rehabilitative interventions has been examined extensively, generating positive yet not completely satisfactory clinical results. This article presents state-of-the-art robotic systems and their prospective function in poststroke rehabilitation of the upper and lower limbs.

A novel robot-aided assist-as-needed gait training paradigm has been developed recently. This paradigm encourages subjects’ active participation during training. Previous pilot studies demonstrated that assist-as-needed robot-aided gait training (RAGT) improves treadmill walking performance post-stroke. However, it is not known if there is an over-ground transfer of the training effects from RAGT on treadmill or long-term retention of the effects. The purpose of the current study was to examine the effects of assist-as-needed RAGT on over-ground walking pattern post-stroke. Nine stroke subjects received RAGT with visual feedback of each subject’s instantaneous ankle malleolus position relative to a target template for fifteen 40-minute sessions. Clinical evaluations and gait analyses were performed before, immediately after and 6 months post-training. Stroke subjects demonstrated significant improvements and some long-term retention of the improvements in their self-selected over-ground walking speed, Dynamic Gait Index, Timed Up and Go, peak knee flexion angle during swing phase and total hip joint excursion over the whole gait cycle for their affected leg (p<0.05). These preliminary results demonstrate that subjects improved their over-ground walking pattern and some clinical gait measures post-training suggesting that assist-as-needed RAGT including visual feedback may be an effective approach to improve over-ground walking pattern post-stroke.

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Most of the control strategies embedded in recent robotic exoskeletons for rehabilitation and assistance are specific implementations of the well-known "assistance as needed" paradigm. A key point in the design of these systems is the requirement for the robot to exert negligible interaction forces to the wearer if he/she is performing well. Optimizing transparency of a device is a challenging task: various strategies have been proposed to achieve this goal, involving both the mechanical structure of the robot and the control algorithms. In this work, we propose a simple yet effective approach that requires minimal redesign efforts in the robotic structure and in the controller to be implemented on existing devices. We experimentally validate the method by comparing kinematic, kinetic and electromyo-graphic data collected from 3 healthy subjects as they walked in three different conditions: free treadmill walking, walking in a robotic trainer with a traditional zero-impedance configuration and walking in the same robot with the new zero-impedance configuration. Results show that the novel configuration was capable of effectively reducing the interaction forces and, as a consequence, it affected subjects' natural gait less than the traditional one did.