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Tensegrities for Assistive & Rehabilitative Healthcare

Compliant tensegrity robots will enable the expansion of medical robots to direct in-home assistive and rehabilitative medical services. Of particular importance is the potential for home health care with our aging population. As of 2010, over 40 million or 13.0% of the U.S. population was of age 65 or older [1]. It is expected that the population for the same age group will be over 92 million or 21.9% of the U.S. population by 2060 [1]. In the meantime, the nation has been chronically short in meeting the demand for nursing professionals [2]. To date, significant amounts of research and development have been performed on robots for health care, such as in-hospital delivery systems [3, 4, 5, 6], specialized rehabilitation machines [7, 8], and surgical robots [9, 10, 11, 12, 13, 14, 15], all of which operate in highly structured hospital settings.

Relatively fewer robots have been built for home assistance [16, 17, 18], with complex collision avoidance algorithms to increase safety. The future will involve direct in-home robotic assistance, which requires robots to be able to safely move through the potentially cluttered and constrained spaces of peoples homes, while safely supporting and interacting with the patient and other family members and pets. This level of competency requires structurally compliant systems capable of adapting to constrained spaces and softly contacting the environment.

Tensegrity robots have the potential for this level of adaptability and safety, and the development of flexible tensegrity spines will enable such robots to easily maneuver through constrained spaces without injuring others, yet also provide the load bearing capability to assist patients with their daily tasks and rehabilitation activities.

See more on biotensegrities.

References Cited

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  2. Heather Janiszewski Goodin. The nursing shortage in the United States of America: an integrative review of the literature. Journal of Advanced Nursing, 43(4):335–343, 2003.
  3. John M Evans. Helpmate: An autonomous mobile robot courier for hospitals. In Intelligent Robots and Systems’ 94.’Advanced Robotic Systems and the Real World’, IROS’94. Proceedings of the IEEE/RSJ/GI International Conference on, volume 3, pages 1695–1700. IEEE, 1994.
  4. Masaki Takahashi, Takafumi Suzuki, Hideo Shitamoto, Toshiki Moriguchi, and Kazuo Yoshida. De- veloping a mobile robot for transport applications in the hospital domain. Robotics and Autonomous Systems, 58(7):889–899, 2010.
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  8. InMotion, Interactive Motion Technologies. http://interactive-motion.com/, accessed 1-20-2014.
  9. Intuitive Surgical. The da Vinci Surgical System. http://intuitivesurgical.com/, accessed 1-20-2014. [70] Iridescent Learning, 2013. http://iridescentlearning.org/, accessed 1-20-2014.
  10. Ben Kehoe, Gregory Kahn, Jeffrey Mahler, Jonathan Kim, Alex Lee, Anna Lee, Keisuke Nakagawa, Sachin Patil, W Douglas Boyd, Pieter Abbeel, and Ken Goldberg. Autonomous multilateral de- bridement with the raven surgical robot. IEEE Int’l Conference on Robotics and Automation, June 2014.
  11. Mitchell JH Lum, Diana CW Friedman, Ganesh Sankaranarayanan, Hawkeye King, Kenneth Fodero, Rainer Leuschke, Blake Hannaford, Jacob Rosen, and Mika N Sinanan. The raven: Design and validation of a telesurgery system. The International Journal of Robotics Research, 28(9):1183–1197, 2009.
  12. Ben Mitchell, John Koo, M Iordachita, Peter Kazanzides, Ankur Kapoor, James Handa, Gregory Hager, and Russell Taylor. Development and application of a new steady-hand manipulator for retinal surgery. In Robotics and Automation, 2007 IEEE International Conference on, pages 623–629. IEEE, 2007.
  13. W Kilby, JR Dooley, G Kuduvalli, S Sayeh, CR Maurer Jr, et al. The CyberKnife robotic radiosurgery system in 2010. Technology in cancer research & treatment, 9(5):433–452, 2010.
  14. I. Nisky, A.M. Okamura, and M. H. Hsieh. Effects of robotic manipulators on movements of novices and surgeons. Surgical Endoscopy, in press, 2014.
  15. A. Okamura and et al.   Stanford University, UC Santa Cruz, UC Berkeley, Johns Hopkins.
  16. Paolo Dario, Eugenio Guglielmelli, Cecilia Laschi, and Giancarlo Teti. MOVAID: a personal robot in everyday life of disabled and elderly people. Technology and Disability, 10(2):77–93, 1999.
  17. Birgit Graf, Matthias Hans, and Rolf D Schraft. Care-O-bot II – development of a next generation robotic home assistant. Autonomous robots, 16(2):193–205, 2004.
  18. Martha E Pollack, Laura Brown, Dirk Colbry, Cheryl Orosz, Bart Peintner, Sailesh Ramakrishnan, Sandra Engberg, Judith T Matthews, Jacqueline Dunbar-Jacob, Colleen E McCarthy, et al. Pearl: A mobile robotic assistant for the elderly. In AAAI workshop on automation as eldercare, volume 2002, pages 85–91, 2002.

Image caption from:  Fascial Fitness: Training in the Neuromyofascial Web, http://www.ideafit.com/fitness-library/fascial-fitness