Shape memory alloy (SMA) actuators are compact and have high force-to-weight ratios, making them strong candidates to actuate robots, exoskeletons, and prosthetics. To optimize speed and energy consumption, SMA actuators have been embedded in an N×N vascular network that can deliver electric and thermofluidic energy to the each actuator. The scalable architecture of the vascular network allows for 2N control devices (valves, transistors) to be shared amongst N² actuators, so that as the number of actuators increases, the number of required control devices scales at a smaller rate. This Network Array Architecture (NAA) allows for each actuator to be controlled individually or in discrete subarrays. However, not all combinations of actuators can be activated simultaneously; therefore in general, a sequence of control commands will be need to be executed in order to achieve the desired actuation. By treating each actuator as having a binary state, the combined states of the actuator array can be represented by graph theory, where states are nodes and the transitions between states are graph edges. By properly weighting the costs of the transitions, graph search techniques can be used to find optimal sets of control commands for desired state changes. This paper formulates the control of NAA actuators systems as a graph theory problem, and characterizes the ability of search algorithms to optimize a weighted combination of speed and energy usage, while minimizing computational cost.

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This paper investigates force accuracy of a human finger in three types of support conditions of an exoskeleton. The exoskeletonaugments pinching force of a wearer’s index finger in proportion to it based on surface electromyography. Three supporting manners of the pinching force are evaluated by switching a fingertip part of the exoskeleton. One is that the assistive force is applied to the wearer’s finger so that the force could be sensible by the wearer. Another case is that the assistive force is directly delivered to a grasping object without a wearer’s fingertip. The other is that a part of the force directly affects the object and the rest affects the wearer’s finger. Through pilot experiments, transitions of the accuracy through training in these cases are compared each other.

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Several manipulators or exoskeleton are characterized by having a concave workspace in the operational space due to mechanical limits. This article proposes an online trajectory planning method for performing visually guided assisted reaching through a rehabilitation robotic exoskeleton, the L-Exos, in its concave workspace. To evalute the proposed methodology in a rehabilitation application, we set-up a computer vision based system that can automatically identify target objects in the workspace and generate a robot assisted movement to reach them through the L-Exos.

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Conventional robotic rehabilitation devices for upper extremity are bulky, heavy or lack the ability to provide joint level rehabilitation. Some designs address these issues by replacing rigid links of the exoskeletons with light weight cables. However these designs are controlled in position mode instead of force control which is desirable for rehabilitation. In this paper, a 5 degree-of-freedom cable-driven upper arm exoskeleton, with control of force, is proposed. In this design, attachment points of cables on the arm are adjustable. The attachment points are optimized to achieve large workspace to perform activities of daily living. Simulation results of force field control for training and rehabilitation of the arm are presented. Experiments have been performed on a dummy robotic arm in the upper arm exoskeleton.

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Background: The overall goal of this paper was to investigate approaches to controlling active participation in stroke patients during robot-assisted gait therapy. Although active physical participation during gait rehabilitation after stroke was shown to im-prove therapy outcome, some patients can behave passively during rehabilitation, not maximally benefiting from the gait training. Up to now, there has not been an effective method for forcing patient activity to the desired level that would most benefit stroke patients with a broad variety of cognitive and biomechanical impairments. Methods: Patient activity was quantified in two ways: by heart rate (HR), a physiological parameter that reflected physical effort during body weight supported treadmill training, and by a weighted sum of the interaction torques (WIT) between robot and patient, recorded from hip and knee joints of both legs. We recor-ded data in three experiments, each with five stroke patients, and controlled HR and WIT to a desired temporal profile. Depending on the patient’s cognitive capabilities, two different approaches were taken: either by allowing voluntary patient effort via visu-al instructions or by forcing the patient to vary physical effort by adapting the treadmill speed. Results: We successfully control-led patient activity quantified by WIT and by HR to a desired level. The setup was thereby individually adaptable to the specific cognitive and biomechanical needs of each patient. Conclusion: Based on the three successful approaches to controlling patient participation, we propose a metric which enables clinicians to select the best strategy for each patient, according to the pati-ent’s physical and cognitive capabilities. Our framework will enable therapists to challenge the patient to more activity by automa-tically controlling the patient effort to a desired level. We expect that the increase in activity will lead to improved rehabili-tation outcome.

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