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Smart Prosthetics Exploring Assistive Devices for the Body and Mind: Task Group Summaries (2007) / Chapter Skim
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Develop a Smart Prosthetic That Can Learn Better and/or Faster
Pages 23-30

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From page 23...
... Even though myoelectric prostheses have better control over body-powered prostheses, these devices involve a steep learning curve for the patients to gain conscious control over the weak electric signals. The finest approach to achieve full control of the prosthetic by the 23
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From page 24...
... This calls for the development of novel methods for measuring large-scale brain activity, learning how to sample and decode motor signals and how to feed them into prostheses to mimic the required movement, new techniques for microstimulating neuronal tissue, developments in microchip design, nano- and microfabrication techniques, and further developments in robotics. • Lack of sensory feedback is another key limitation that seriously hinders the ability of the prosthesis to respond to external environment.
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From page 25...
... Wilkoff Professor of Electrical and Computer Engineering and Robotics; Director, Institute for Complex Engineered Systems, Carnegie Mellon University • Brent Gillespie, Assistant Professor, Mechanical Engineering, University of Michigan • Anne Heberger, Research Associate, National Academies Keck Futures Initiative • Hod Lipson, Assistant Professor, Mechanical and Aerospace Engineering, Cornell University • Yoky Matsuoka, Associate Professor, Computer Science and Engineering, University of Washington • Michael Merzenich, Francis Sooy Professor of Otolaryngology, Keck Center for Integrative Neurosciences, University of California, San Francisco, School of Medicine • Santa Ono, Vice Provost and Deputy Provost, Emory University • Kevin Otto, Assistant Professor, Weldon School of Biomedical Engineering and Biological Sciences, Purdue University
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From page 26...
... This is possible due to a feedback loop, which allows the model to act differently depending on what its previous actions have accomplished. The second definition defines learning as the act of organizing, or reorganizing, neural circuits so one can successfully interpret information and/or external signals and respond appropriately.
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From page 27...
... Day 2 On the second day of the conference, our group had only two densely packed hours to solidify our definition of learning, consider how brain and machine should share the learning, begin considering what different customers will require of their prosthetic device, and decide what will be required to accomplish our assigned task. After much discussion, the group agreed that learning is the method of reorganizing human neural circuits in the prosthetic device to interpret signals and generate cognitive/motor outputs.
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From page 28...
... Day 3 On day three the group defined learning issues for the client, the prosthetic device, and the training program, which includes hardware and software. For the client, learning issues include training strategies with neurophysiologic foundations, providing sensory stimuli, and monitoring the learning progress.
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From page 29...
... The device must be dynamically adaptive to the client and incorporate contextual information flow and should employ multiple motor outputs to enable a rich repertoire of tasks. This involves gradually enabling the degrees of freedom, as discussed earlier, and the complexity, as well as enabling posture balance, stability, and movement.
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From page 30...
... These research priorities were divided into the client, prosthetic device, and training program groups. Research is necessary to determine how the brain learns to handle rich sensory inputs as well as how the brain translates user intent to motor action for increasingly sophisticated function.
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Key Terms

  • smart prosthetic
  • learning issues
  • technology gaps
  • key features
  • user intent

  • dumb prosthetic
  • promote learning
  • rich sensory
  • machine learning
  • human brain

  • sensory information
  • motor outputs
  • artificial prostheses
  • academies keck
  • neural circuits

  • brain activity
  • keck futures
  • information flow
  • prostheses using
  • futures initiative

  • smarter prosthetic
  • emory university
  • electric signals
  • preferred option
  • electroactive polymeric

  • posture balance
  • sophisticated function
  • polymeric sensors
  • dynamically adaptive
  • francis sooy


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