University of Twente Student Theses


Development of an advanced control module for context-aware upper-limb prostheses

Bravo Cabrera, Miguel Angel (2020) Development of an advanced control module for context-aware upper-limb prostheses.

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Abstract:Nowadays, upper-limb prostheses have reached an extraordinary level of sophistication. However, the limitations of the state-of-the-art myo-electric control algorithms are not capable to driving the large number of degrees of freedom (DOFs) that they present. The development of collaborative approaches, in which some of these DOFs are automated, is a trend in the last years. Yet, there are no evidences on how the combination of the control inputs has to be implemented in order to maximize the benefit of the users. With this study, we provide a sight into the effects that different control mixing schemes have on the time performance and the cognitive workload of their users, when performing reach-to-grasp tasks. To this aim, we have developed a semi-autonomous control system, a manual control system and three different control mixing schemes: a master-slave scheme and two simultaneous control approaches, namely, one in which the automation activates when there is manual control, and one independent of it. We found that the simultaneous control, when the automation is independent of the user controlling any DOF, was the fastest option for all the participants, decreasing the task elapsed times up to 70% (p<0.05) when compared to manual control. Moreover, all the participants showed reductions in the cognitive workload of up to 82% respect to manual control (p<0.05). The master-slave scheme provided similar results. When the system required the participants to drive at least one DOF for the automation to happen, the automation did not provide benefit compared to manual control in terms of workload. The differences shown across control mixing schemes, in the outcome measurements, demonstrate their implications require profound for the development of collaborative control systems.
Item Type:Essay (Master)
Universitaetmedizin Goettingen, Goettingen, Germany
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:50 technical science in general
Programme:Electrical Engineering MSc (60353)
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