Development of a magnetorheological hand exoskeleton featuring a high force-to-power ratio for enhanced grip endurance
Source abstract
Abstract
Abstract Hand exoskeletons have significant potential in labor-intensive fields by mitigating hand grip fatigue, enhancing hand strength, and preventing injuries. However, most of the traditional hand exoskeletons are driven by motors, whose output force is limited in the constrained installation conditions. Besides, they also come with the disadvantages of high power consumption, complex and bulky assistive systems, and high instability. In this work, we develop a novel hand exoskeleton integrated with innovative magnetorheological (MR) clutches that offers a high force-to-power ratio to improve grip endurance. The clutch features an enhanced structure design, a micro roller enhancing structure, which can significantly boost output forces. The experimental data demonstrate that, when it is supplied with 2 V, the clutch can deliver a peak holding force of 381.15 N—55 times that when no voltage is provided (7 N). In this scenario, it only consumes 1.38 W, yielding a force-to-power ratio of 256.75 N W −1 , which is 2.35 times higher than the best-reported actuator used for hand exoskeletons. This capability enables the designed MR exoskeleton (MRHE) to provide approximately 419.79 N support force for gripping. The designed MR hand exoskeleton is highly integrated, comprising an exoskeleton frame, MR clutches, a control unit, and a battery. Evaluations through static grip endurance tests and dynamic carrying and lifting tests confirm that the MR hand exoskeleton can effectively reduce muscle fatigue, extend grip endurance, and minimize injuries. These findings highlight its strong potential for practical applications in repetitive tasks such as carrying and lifting in industrial settings.
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Research context
OpenAlex topics: Prosthetics and Rehabilitation Robotics · Vibration Control and Rheological Fluids · Muscle activation and electromyography studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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