Biomechanical loading, in particular, viscous loading of the upper limb has been proposed in the literature as a means for suppressing pathologic tremor. It is expected that an improvement on manipulative function can be obtained by reducing the tremorous motion associated to some neurological disorders. This article presents two non-grounded control strategies to suppress tremor by means of a orthotic (wearable) exoskeleton. These two strategies are based on biomechanical loading and notch filtering of tremor via internal forces. Both controls strategies are evaluated and validated on the robotic exoskeleton called WOTAS (wearable orthosis for tremor assessment and suppression). At the end, results obtained in the pre-clinical trials and conclusions of this study are presented.

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The Berkeley lower extremity exoskeleton (BLEEX) is a load-carrying and energetically autonomous human exoskeleton that, in this first generation prototype, carries up to a 34 kg (75 Ib) payload for the pilot and allows the pilot to walk at up to 1.3 m/s (2.9 mph). This article focuses on the human-in-the-loop control scheme and the novel ring-based networked control architecture (ExoNET) that together enable BLEEX to support payload while safely moving in concert with the human pilot. The BLEEX sensitivity amplification control algorithm proposed here increases the closed loop system sensitivity to its wearer’s forces and torques without any measurement from the wearer (such as force, position, or electromyogram signal). The tradeoffs between not having sensors to measure human variables, the need for dynamic model accuracy, and robustness to parameter uncertainty are described. ExoNET provides the physical network on which the BLEEX control algorithm runs. The ExoNET control network guarantees strict determinism, optimized data transfer for small data sizes, and flexibility in configuration. Its features and application on BLEEX are described.

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The exoskeleton is an autonomous robotic device whose function is to increase the strength and endurance of a human pilot. In order to achieve an exoskeleton controller which reacts compliantly to external forces, an accurate model of the dynamics of the system is required. In this report, a series of system identification experiments are designed and carried out for the Berkeley lower extremity exoskeleton. As well as determining the mass and inertia properties of the segments of the legs, various non-ideal elements such as friction, stiffness and damping forces are identified. The resulting dynamic model is found to be significantly more accurate than the original model predicted from the designs of the robot.

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Metabolic studies have shown that there is a metabolic cost associated with carrying load. Several leg exoskeletons have been developed by various groups in an attempt to augment the load carrying capability of the human. Previous research efforts have not fully exploited the passive dynamics of walking and have largely focused on fully actuated exoskeletons that are heavy with large energy requirements. In this paper, a lightweight, underactuated exoskeleton design is presented that runs in parallel to the human and supports the weight of a payload. Two exoskeleton architectures are pursued based on examining human walking data. A first architecture consists of springs at the hip, a variable impedance device at the knee, and springs at the ankle. A second architecture replaces the springs at the hip with a non-conservative actuator to examine the effect of adding power at desired instances throughout the gait cycle. Preliminary studies show that an efficient, underactuated leg exoskeleton can effectively transmit payload forces to the ground during the walking cycle.

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To assist physically disabled, injured, and/or elderly persons, we have been developing a 3DOF exoskeleton robot for assisting upper-limb motion, since upper-limb motion is involved in a lot of activities of everyday life. The exoskeleton robot is mainly is controlled by the skin surface electromyogram (EMG) signals, since EMG signals of muscles directly reflect how the user intends to move. This paper introduces the mechanism of the exoskeleton robot and also proposes a control method of the exoskeletonrobot considering the generated end-effector force vectors.

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