Background: Practicing arm and gait movements with robotic assistance after neurologic injury can help patients improve their mo- vement ability, but patients sometimes reduce their effort during training in response to the assistance. Reduced effort has been hypothesized to diminish clinical outcomes of robotic training. To better understand patient slacking, we studied the role of visu-al distraction and auditory feedback in modulating patient effort during a common robot-assisted tracking task. Methods: Fourteen participants with chronic left hemiparesis from stroke, five control participants with chronic right hemiparesis and fourteen non-impaired healthy control participants, tracked a visual target with their arms while receiving adaptive assistance from a robotic arm exoskeleton. We compared four practice conditions: the baseline tracking task alone; tracking while also performing a visual distracter task; tracking with the visual distracter and sound feedback; and tracking with sound feedback. For the distracter task, symbols were randomly displayed in the corners of the computer screen, and the participants were instructed to click a mouse button when a target symbol appeared. The sound feedback consisted of a repeating beep, with the frequency of repetition made to increase with increasing tracking error. Results: Participants with stroke halved their effort and doubled their tracking error when per-forming the visual distracter task with their left hemiparetic arm. With sound feedback, however, these participants increased their effort and decreased their tracking error close to their baseline levels, while also performing the distracter task suc-cessfully. These effects were significantly smaller for the participants who used their non-paretic arm and for the participants without stroke. Conclusions: Visual distraction decreased participants effort during a standard robot-assisted movement training task. This effect was greater for the hemiparetic arm, suggesting that the increased demands associated with controlling an af-fected arm make the motor system more prone to slack when distracted. Providing an alternate sensory channel for feedback, i.e., auditory feedback of tracking error, enabled the participants to simultaneously perform the tracking task and distracter task effec-tively. Thus, incorporating real-time auditory feedback of performance errors might improve clinical outcomes of robotic therapy systems.

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The unique exoskeleton system (EXO-UL7) in UCSC is controlled in two levels. The lower-level uses standard PID control. Three force sensors in the upper-level send desired trajectories to the lower-level. The impedance/admittance control can is limit both internal and contact forces. It is impossible to design a model-based impedance/admittance control when the model of theexoskeleton is unavailable. In this paper, a model-free PID type admittance control is applied, whose parameters can be designed by human impedance properties. The inverse kinematics are required when the desired trajectories generated by admittance control in task space. It is difficult to solve the inverse kinematics problem especially when the robots are redundant, such asexoskeleton system. In this paper, we put both the upper-level PID admittance control and the lower-level linear PID control in task space. Novel sufficient conditions of semiglobal asymptotic stability are proposed via stability analysis in task space. These conditions give an explicit selection method of PID gains.

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A neurobot (NR) is a mechatronic wearable robot that can be applied to drive a paralyzed limb. Through the application of controllable forces, a NR can assist, replace, or retrain a certain motor function. Robotic intervention in rehabilitation of motor disorders has a potential to improve traditional therapeutic interventions. Because of its flexibility, repeatability and quantifiability, NRs have been more and more applied in neurorehabilitation. Furthermore, combination of NRs with functional electrical stimulation/therapy constitutes a trend to overcome a number of practical limitations to widespread the application of NRs in clinical settings and motor control studies. In this review, we examine the motor learning principles, robotic control approaches and novel developments from studies with NRs and hybrid systems, with a focus on rehabilitation of the lower limbs.

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This paper presents the design and simulation of a cyclic robot for lower-limb exercise robots. The robot is designed specifically for cyclic motions and the high power nature of lower-limb interaction—as such, it breaks from traditional robotics wisdom by intentionally traveling through singularities and incorporating large inertia. Such attributes lead to explicit design considerations. Results from a simulation show that the specific design requires only a reasonably sized damper and motor.

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This paper presents a dynamic model of a knee joint interacting with a two-link exoskeleton for investigating the effects of different exoskeleton designs on internal joint forces. The closed kinematic chain of the leg and exoskeleton has a significant effect on the joint forces in the knee. A bio-joint model is used to capture this effect by relaxing a commonly made assumption that approximates a knee joint as a perfect engineering pin-joint in exoskeleton design. Based on the knowledge of a knee-joint kinematics, an adaptive knee-joint exoskeleton has been designed by incorporating different kinematic components (such as a pin, slider and cam profile). This design potentially eliminates the negative effects associated with the closed leg/exoskeletonkinematic chain on a human knee. An investigation in the flexion motion of an artificial human knee joint is presented to compare performances of five exoskeleton designs against the case with no exoskeletons. Analytical results that estimate internal forces using the dynamic model (based on the properties of a knee joint) agree well with the experiments. These studies lead to an adaptive mechanism with a slider/cam as an alternative to pin joints for the exoskeleton, and illustrate the application of the model for designing an adaptive mechanism that minimizes internal joint forces due to a human-exoskeleton interaction.

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