Matching Items (2)
Filtering by
- Creators: Berman, Spring
- Creators: Black Jr., John A
Description
Myoelectric control is lled with potential to signicantly change human-robot interaction.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
ContributorsIson, Mark (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Greger, Bradley (Committee member) / Berman, Spring (Committee member) / Sugar, Thomas (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2015
Description
All too often, industrial designers face seemingly intractable obstacles as they endeavor to, as Simon (1996, p. 111) describes, devise "courses of action aimed at changing existing situations into preferred ones." These problems, described by Rittel and Webber (1973) as "wicked," are insurmountable due to the contradictory and changing nature of their requirements. I argue that that industrial design (ID) is largely subject to Rittel's quandary because of its penchant for producing single solutions for large populations; such design solutions are bound, in some senses, to fail due to the contradictory and changing nature of large and, thus, inherently diverse populations. This one-size-fits-all approach is not a necessary attribute of ID, rather, it is a consequence of the time in which it came into being, specifically, the period of industrial mass production. Fortunately, new, agile manufacturing techniques, inexpensive sensors, and machine learning provide an alternative course for ID to take, but it requires a new way of thinking and it requires a new set of methods, which I will elaborate in this thesis. According to Duguay, Landry, and Pasin (1997), we are entering an age where it will be feasible to produce individualized, one-off products from large-scale industrial manufacturing facilities in a way that is not only cost effective, but in many ways as cost effective as the existing techniques of mass production. By availing ourselves of these opportunities, we can tame the problem, not by defeating Rittel's logic, rather by reducing the extent to which his theories are appropriate to the domain of ID. This thesis also describes a test study: an experiment whose design was guided by the proposed design methodologies. The goal of the experiment was to determine the feasibility of a noninvasive system for measuring the health of the forearm muscles. Such a tool would provide the basis for assessing the true impact and possible pathogeny of the manual use of products or modifications to products. Previously, it was considered impossible to use surface electromyography (as opposed to needle or wire based electromyography) to assess muscular activity and muscular health due to the complexity of the arrangement of muscles in the forearm. Attempts to overcome this problem have failed because they have tried to create a single solution for all people. My hypothesis is that, by designing for each individual, a solution may be found. Specifically, I show that, for any given individual, there is a high correlation between the EMG signal and the movements of the fingers that, ostensibly, those muscles control. In other words, by knowing, with great accuracy, the position and the motion of the hand then it would become possible to disambiguate the mixed signals coming from the complex web of muscles in the forearm and enable the assessment of the forearm's health by non-invasive means.
ContributorsBraiman, Stuart (Author) / Giard, Jacques (Thesis advisor) / Black Jr., John A (Committee member) / Herring, Donald (Committee member) / Arizona State University (Publisher)
Created2012