Clinicians and scientists often focus on tracking the recovery of motor skills after spinal cord injury (SCI), but less attention is paid to the recovery of sensory skills. Measures of sensory function are imperative for evaluating the efficacy of treatments and therapies. Proprioception is one sensory modality that provides information about static position and movement sense. Because of its critical contribution to motor control, proprioception should be measured during the course of recovery after neurological injury. Current clinical methods to test proprioception are limited to crude, manual tests of movement and position sense. The purpose of this study was to develop a quantitative assessment tool to measure joint position sense in the legs. We used the Lokomat, a robotic exoskeleton, and custom software to assess joint position sense in the hip and knee in 9 able-bodied (AB) subjects and 1 person with incomplete SCI. We used two different test paradigms. Both required the subject to move the leg to a target angle, but the presentation of the target was either a remembered or visual target angle. We found that AB subjects had more accurate position sense in the remembered task than in the visual task, and that they tended to have greater accuracy at the hip than at the knee. Position sense of the subject with SCI was comparable to those of the AB subjects. We show that using the Lokomat to assess joint position sense may be an effective clinical measurement tool.

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This paper presents the kinematic design of a single degree-of-freedom exoskeleton mechanism: a planar eight-bar mechanism for finger curling. The mechanism is part of a finger-thumb robotic device for hand therapy that will allow users to practice key pinch grip and finger-thumb opposition, allowing discrete control inputs for playing notes on a musical gaming interface. This approach uses the mechanism to generate the desired grasping trajectory rather than actuating the joints of the fingers and thumb independently. In addition, the mechanism is confined to the back of the hand, so as to allow sensory input into the palm of the hand, minimal size and apparent inertia, and the possibility of placing multiple mechanisms side-by-side to allow control of individual fingers.

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This paper proposes a human machine interface for assistive exoskeleton based on face analysis. The 4 DoF assistive robotic system designed is dedicated to people suffering from myopathy and aims to compensate for the loss of mobility in the upper limb. The proposed interface is able to convert user head gesture and mouth expression into a suitable control command. Moreover, we propose a visual context analysis component to make a more accurate command. The tests conducted show that the use of vision based interface is particularly adapted to disabled people. In this paper, we will first describe the problematic and the designed mechanical system. Next, we will describe the two approaches developed for visual sensing interface: head control and mouth expression control. We will focus on mouth extraction algorithm. Finally, we introduce the context detection for scene understanding.

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Based on the analyze of the assist walking exoskeleton leg behavior characteristics, the total cource locomotion can be divided into swing and stance two phase. To the stance phase, the position control based on fixed gravity compensation is used. To the swing phase, the fast terminal sliding mode control is used to design the virtual torque controller. Theoretical analyse and simulation results test the feasibility and validity of this control method.

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The required tasks in fixed-base exoskeletons demand a fast position/force controller; yet robust against unknown disturbances due to the application itself is tightly coupled with a human in a wide range of operational conditions, which give rise to human-exoskeleton interaction dynamics, high nonlinear uncertain exoskeleton dynamics, noisy sensors and other parametric uncertainties, such as environmental contacts. These factors do not allow to account on a precise dynamical model, thus model-based (regressor-based) controllers are difficult to implement. This paper deals with a regressor-free smooth PID-like fast force/position controller which guarantees finite-time convergence within second order sliding modes, thus ensuring inherent robustness. Experimental platform allows assessing its performance for rehabilitation tasks, which validates its functionality in practical implementation.

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