The Dampace exoskeleton combines functional exercises resembling activities of daily living with impairment-targeted force-coordination training. The goal of this paper is to evaluate the performance of the Dampace. In the design, the joint rotations are decoupled from the joint translations; the robot axes align themselves to the anatomical axes, overcoming some of the traditional difficulties of exoskeletons. Setup times are reduced to mere minutes and static reaction forces are kept to a minimum. The Dampace uses hydraulic disk brakes, which can resist rotations with up to 50 N m and have a torque bandwidth of 10 Hz for multisine torques of 20 N m. The brakes provide passive control over the movement; the patients’ movements can be selectively resisted, but active movement assistance is impossible and virtual environments are restricted. However, passive actuators are inherently safe and force active patient participation. In conclusion, the Dampace is well suited to offer force-coordination training with functional exercises.

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An electromyography (EMG) signal based fuzzy-neuro control method is proposed in this paper for a human upper-limb motion assist exo-skeleton robot. The upper-limb exoskeleton robot (named W-EXOS) assists the motions of human forearm pronation/supination, wrist flexion/extension and ulnar/radial deviation. The paper presents the EMG signal based fuzzy-neuro control method with multiple fuzzy-neuro controllers and the adaptation method of the controllers. The skin surface EMG signals of muscles in the forearm of the exoskeleton user and the hand force/forearm torque measured from the sensors of the exoskeleton robot are used as input information for the controllers. Fuzzy-neuro control method, which is a combination of flexible fuzzy control and adaptive neural network cont-rol, has been applied to realize the natural and flexible motion assist. In the control method, multiple fuzzy-neuro controllers are applied, since the muscle activation levels change in accordance with the angles of motions. The control method is able to adapt in accordance with the changing EMG signal le-vels of different users. Experiments have been performed to evaluate the propo-sed control method.

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To guide development of robotic lower limb exoskeletons,it is necessary to understand how humans adapt topowered assistance. The purposes of this study were to quantify joint moments while healthy subjects adapted to a robotic ankle exoskeleton and to determine if the period of motor adaptation is dependent on the magnitude of robotic assistance. The pneumatically powered ankle exoskeleton provided plantar flexor torque controlled by the wearer’s soleus electromyography (EMG). Eleven na¨ıve individuals completed two 30-min sessions walking on a split-belt instrumented treadmill at 1.25m/s while wearing the ankle exoskeleton. After two sessions of practice, subjects reduced their soleus EMG activation by 36% and walked with total ankle moment patterns similar to their unassisted gait (r2=0.9870.02, THSD, p40.05). They had substantially different ankle kinematic patterns compared to their unassisted gait (r2=0.7970.12,THSD, po0.05). Not all of the subjects reached a steady-state gait pattern within the two sessions, in contrast to a previous study using a weaker robotic ankle exoskeleton (Gordon and Ferris,2007). Our results strongly suggest that humans aim for similar joint moment patterns when walking with robotic assistance rather than similar kinematic patterns. In addition, greater robotic assistance provided during initial use results in a longer adaptation process than lesser robotic assis-tance.

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Recent experimental evidence suggests that a fundamental property of the human motor system is that it “slacks”; that is, that it continuously attempts to decrease levels of muscle activation when movement error is small during repetitive motions. This paper reviews several computational models of slacking, and discusses implications of slacking for the design of robotic ortho-ses. For therapeutic applications of robotic orthoses, slacking may reduce human effort during rehabili-tation training, with negative consequences for usedependent motor recovery. For assistive applications of robotic orthoses, slacking may allow the motor system to learn to take advantage of force amplification provided by an orthosis, with positive consequences for human energy efficiency.

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This paper presents the design of a wearable upper arm exoskeleton that can be used to assist and train arm movements of stroke survivors or subjects with weak musculature. In the last ten years, a number of upper-arm training devices have emerged. However, due to their size and weight, their use is restricted to clinics and research laboratories. Our proposed wearable exoskeleton builds upon our extensive research experience in wire driven manipulators and design of rehabilitative systems. The exoskeleton consists of three main parts: an inverted U-shaped cuff that rests on the shoulder, a cuff on the upper arm, and a cuff on the forearm. Six motors, mounted on the shoulder cuff, drive the cuffs on the upper arm and forearm, using cables. In order to assess the performance of this exoskeleton, prior to use on humans, a laboratory test-bed has been developed where this exoskeleton is mounted on a model skeleton, instrumented with sensors to measure joint angles and transmitted forces to the shoulder. This paper describes design details of the exoskeleton and addresses the key issue of parameter optimization to achieve useful workspace based on kinematic and kinetic models.

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