Advertisement

Human Performance and Rehabilitation Technologies

  • Jack M. Winters
  • Corinna Lathan
  • Sujat Sukthankar
  • Tanja M. Pieters
  • Tariq Rahman
Chapter

Abstract

Sections VIII and IX of this book differ from the previous sections in that they are tied more closely to applied research, especially as related to rehabilitation. This seems appropriate. When addressing the significance of our work, most of us include a statement that our research will ultimately help enhance the quality of life of certain types of persons with disabilities. Often the motivation behind our claim is that the increased knowledge obtained from our collective basic research will ultimately lead to technological or therapeutic innovation that will benefit society. This concept has deep roots that go back to the influential writings of Vannevar Bush, a U.S. presidential science adviser during the 1940s, who helped spawn dramatic increases in government-sponsored research infrastructure (e.g., the creation of NSF and NIH), and subsequently in the number of research-oriented scientists and engineers within most developed societies.

Keywords

Virtual Reality Human Performance Functional Independence Measure Rehabilitation Technology Fuzzy Expert System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allard, P., Stokes, I.A.F., and Blanchi, J-P. (eds.) (1995). Three-Dimensional Analysis of Human Movement. Human Kinetics, Champaign.Google Scholar
  2. Allen, J.R., Karchak, A., and Nickel, V.L. (1970). Orthotic Manipulators. In Advances in External Control of Human Extremities. Belgrade.Google Scholar
  3. Ascension Technology. (1995). Flock of birds user’s manual.Google Scholar
  4. Barter, J.T. (1957). Estimation of the mass of body segments. WADC Techn. Report 57–260, Wright-Pattern A.F.B., Aero. Med. Lab., Ohio.Google Scholar
  5. Barto, A.G., Sutton, R.S., and Watkins, C.J.C.H. (1983). Neuronlike elements that can solve difficult learning control problems. IEEE Trans. Sys. Man Cybern., 13:835–846.Google Scholar
  6. Bernstein, N.A. (1935). Biodynamics of walking of normal adult man. Moscow, Orignal in Russian, partial translation be B. Bresler, Prosthetics Devices Research Project, University of California, Berkeley, 1947.Google Scholar
  7. Birch et al., G.E. (1996). An assessment methodology and its application to a robotic vocational assistive device. Tech. Disabil., 5:151–166.CrossRefGoogle Scholar
  8. Bogner, M.S. (ed.). (1994). Human error in medicine. Lawrence Erlbaum Associates. Hillsdale, New Jersey.Google Scholar
  9. Braune, W. and Fischer, O. (1895). Versuche am unbelasteten und belasteten Menschen. Vol. 21, pp. 151–324.Google Scholar
  10. Bresler, B. and Frankel, J.P. (1950). The forces and moments in the leg during level walking. Trans. ASME, 72:27–36.Google Scholar
  11. Bresler, B., Radcliffe, C.W., and Berry, F.R. (1957). Energy and power in the legs of above-knee amputees during normal level walking. Inst. Eng. Res., University of California, Berkeley, Series 11, issue 31, pp. 26.Google Scholar
  12. Budde, J.P. (1990). Independent living centers: a parallel resource. In Rehabilitation Engineering. Smith, R.V. and Leslie, J.H. (eds). CRC Press, Boca Raton.Google Scholar
  13. Burdea, G. and Coiffet, P. (1994). Virtual Reality Technology, John Wiley & Sons, New York.Google Scholar
  14. Caldwell, D.G. (1993). Natural and artificial muscle elements as robot actuators. Mechatronics, 3:269–283.CrossRefGoogle Scholar
  15. Card, S., Moran, T., and Newell, A. (1983). The psychology of human-computer interaction. Lawrence Erlbaum Associates, Hillsdale, New Jersey.Google Scholar
  16. Card, S., Moran, T., and Newell, A. (1986). The model human processor. In Handbook of Perception and Human Performance. Boff, K., Kaufman, L., and Thomas, J. (eds.), vol 2. John Wiley & Sons, New York.Google Scholar
  17. Carlet, M.G. (1872). Essai experimental sur la locomotion humaine. Annales des Sciences Naturelles, Section Zoologique, Series 5, 16:1–93.Google Scholar
  18. Chen, S., Harwin, W., and Rahman, T. (1994). The application of discrete-time adaptive impedance control to rehabilitation robot manipulators. Proc. of IEEE Int’l Conf on Robotics and Automation, San Diego, May.Google Scholar
  19. Childress, D.S. (1985). Historical aspects of powered upper-limb prostheses. Clin. Prosth. Orthotics., 9:2–13.Google Scholar
  20. Christensen, J.M., Topmiller, D.A., and Gill, R.T. (1988). Human factors definitions revisited. Human Factors Society Bulletin, 31, pp. 7–8.Google Scholar
  21. Clark, M. and Stark, L. (1975). Time optimal behavior of human saccadic eye movement. IEEE Trans. Autom. Control AC-20:345–348.CrossRefGoogle Scholar
  22. Cook, A.M. and Hussey, S.M. (1995). Assistive technologies: principles and practice. Mosby, St. Louis.Google Scholar
  23. Cunningham, D.M. (1950). Components of floor reactions during walking. Prosthetic Devices Research Project, Inst. Eng. Res., University of California, Berkeley, Series 11, issue 14.Google Scholar
  24. Cummingham, D.M. and Brown, G.W. (1952). Two devices for measuring the forces acting on the human body during walking. Proc. Soc. Exp. Stress Analysis, 9:75–90.Google Scholar
  25. Cyberedge Journal (1993). Product of the year. Sausolito, California, March/April, pp. 3–5.Google Scholar
  26. Davies, A.R., Doyle, M.A., Lansky, D., Rutt, W., Stevic, M.O., and Doyle, J.B. (1994). Outcomes assessment in clinical settings: a consensus statement on principles and best practices in project management. The Joint Commission on Accreditation of Healthcare Organizations, pp. 6–16.Google Scholar
  27. Dempster, W.T. (1955). Space requirements of the seated operator. WADC Techn. Report 55–159, Wright-Patterson A.F.B., Ohio.Google Scholar
  28. Doubler, J.A. and Childress, D.S. (1984). An analysis of a prosthesis control system based on the concept of extended physiological proprioception. J. Rehab. Res. Dev., 21:1–18.Google Scholar
  29. Downton, A. (ed). (1991). Engineering the Human-computer Interface. McGraw-Hill, London.Google Scholar
  30. Evans, M. (1988). MAGPIE—a lower-limb-operated manipulator. Engng. Med., 17:81.CrossRefGoogle Scholar
  31. Fee, J. et al. (1995). The consumer innovation laboratory: an exercise in consumer empowerment. RESNA Proceedings, 15: 502–504. Vancouver, June.Google Scholar
  32. Fitts, P.M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psych., 47:381–391.CrossRefGoogle Scholar
  33. Franier, R., Groleau, N., Hazerlton, L., Colombano, S., Compton, M., Statler, I., Szolovits, P., and Young, L. et al. (1994). PI-in-a-box: a knowledge-based system for space science experimentation. AI Magazine, pp. 39–51.Google Scholar
  34. Galvin, J. (1991). The history of rehabilitation engineering. Assistive Technol. Google Scholar
  35. Gavrilovic, M.M. and Maric, M.R. (1969). Positional servo-mechanism activation by artificial muscles. Med. Biol Eng., 7:77–82.PubMedCrossRefGoogle Scholar
  36. General Reality Inc. (1996). Product catalog.Google Scholar
  37. Glanville, A.D. and Kreezer, G. (1937). The characteristics of gait of normal male adults. J. Exp. Psychol., 21:277–301.CrossRefGoogle Scholar
  38. Granger, C.V. and Brownscheidle, C.M. (1995). Outcome measurement in medical rehabilitation. Int. J. Technol. Assess. Health Care, 11:262–268.PubMedCrossRefGoogle Scholar
  39. Groleau, N. (1994). ASSET: automation and support system for expert tele-sciences. Techn. Descrip. Doc., M/S 269-2, Artif. Intel. Res. Branch, NASA-AMES Research Center, Moffett Field.Google Scholar
  40. Hannaford, B. and Winters, J.M. (1990). Actuator properties and movement control: biological and technological models. In Multiple Muscle Systems. Winters, J.M. and Woo, S.L-Y. (eds.), pp. 101–120. Springer-Verlag, New York.Google Scholar
  41. Hannaford, B., Winters, J.M., Chou, C-P., and Marbot, P-H. (1995). The anthroform biorobotic arm: a system for the study of spinal circuits. Ann. Biomed. Eng., 23:359–374.CrossRefGoogle Scholar
  42. Heckathorne, C.W. (1990). Manipulation in unstructured environments: extended physiological proprioception, position control, and arm prostheses. Proc. Int. Conf. Rehab. Robotics, pp. 25–40. Wilmington.Google Scholar
  43. Hodges, L.F., Bolter, J., Mynatt, E., Ribarsky, W., and van Teylingen, R. (1993). Virtual environment research at the Georgia Tech GVU Center. Presence, 2(3):234–243.Google Scholar
  44. Hogan, N. (1984). Adaptive control of mechanical impedance by coactivation of antagonist muscles. IEEE Trans. Autom. Control, AC-29:681–690.CrossRefGoogle Scholar
  45. Hogan, N. (1990). Mechanical impedance of single- and multi-articular systems. In Multiple Muscle Systems. (Winters, J.M. and Woo, S.L-Y. (eds.), pp. 149–164. Springer-Verlag, New York.Google Scholar
  46. Hogan, N. and Winters, J.M. (1990). Principles underlying movement organization: upper limb. In Multiple Muscle Systems. Winters, J.M. and Woo, S.L-Y. (eds.), pp. 182–194. Springer-Verlag, New York.Google Scholar
  47. Howell, R., et al. (1996). Classroom applications of educational robots for inclusive teams of students with and without disabilities. Technol. Disabil., 5:139–150.CrossRefGoogle Scholar
  48. Inman, V.T., Ralston, H.J., Saunders, J.B., Feinstein, B., and Wright, E.W. (1952). Relation of human electromyogram to muscular tension. Electroenceph. Clin. Neurophysiol., 4:187–194.PubMedCrossRefGoogle Scholar
  49. Innman, V. T., Saunders, J.B., and Abbot, L.C. (1944). Observations on the function of the shoulder joint. J. Bone Joint Surg., 26-A:1–30.Google Scholar
  50. Johnston, R.S. (1987). The SIMNET visual system, Proc. Ninth ITEC Conf., pp. 264–273. Washington, DC.Google Scholar
  51. Kazi, Z. et al. (1996). Multimodally controlled intelligent assistive robot. RESNA Proc., vol. 16, pp. 348–350. Salt Lake City, June.Google Scholar
  52. Kijima, R., Shirakawa, K., Hirose, M., and Nihei, K. (1994). Virtual sand box: development of an application of virtual environments in clinical medicine. Presence, 3(1):45–59.Google Scholar
  53. Kroemer, K., Kroemer, H., Kroemer-Elbert, K. (1994). Ergonomics. Prentice Hall, Englewood Cliffs, New Jersey.Google Scholar
  54. Kruit, J. and Cool, J.C. (1989). Body-powered hand prosthesis with low operating power for children. J. Med. Eng. & Technol., 13:129–133.CrossRefGoogle Scholar
  55. LeBlanc, M.A. (1987).Google Scholar
  56. Lamoreux, L.W. (1971). Kinematic measurements in the study of human walking. Bull. Prosthetic Res., BPR 10–15, pp. 3–84.Google Scholar
  57. Lamson, R.J. (1995). Clinical application of virtual therapy to psychiatric disorders. Virtual Reality and Persons with Disabilities. San Fransisco.Google Scholar
  58. Levins, A.S., Inman, V.T., and Blosser, J.A. (1948). Transverse rotation of the segments of the lower extremity in locomotion. J. Bone Joint Surg., 30-A:859–872.Google Scholar
  59. Lin, C-T. and Lee, SC.S.G. (1996). Neural fuzzy systems. A neuro-fuzzy synergism to intelligent systems. Prentice Hall, Upper Saddle River, New Jersey.Google Scholar
  60. Liu, A., Tharp, G., French, L., Lai, S., and Stark, L. (1993). Some of what oneneeds to know about using head-mounted displays to improve teleoperator performance. IEEE Trans Robotics Auto., 9:638–648.CrossRefGoogle Scholar
  61. Marquardt, E. (1961). Biomechanical control of pneumatic prostheses with special consideration of the sequential control. In Application of External Power in Prosthetics and Orthotics. Publ. 874, NAS-RC, pp. 20–31.Google Scholar
  62. Mason, C.P. and E. Peiser. (1979). A seven degree of freedom telemanipulator for tetraplegics. Conf. Int. sur les Telemanipulators pour Handicapes Physiques, pp. 309–318.Google Scholar
  63. McDonough, J. (1993). Doorways to the virtual battlefield. Proc. Virtual Reality,’92, 104–114.Google Scholar
  64. Michael, J. (1986). Upper limb powered components and control: current concepts. Clin. Prosth. Orthotics, 10:66–77.Google Scholar
  65. Monheit, G. and N. Badler (1991). A kinematic model of the human spine and torso. IEEE Comput. Graphics Appl., Noton, D. and Stark, L. (1971). 11(2):29–38, 1991.Google Scholar
  66. NASA (1993). Virtual Technology for Training. Technical Report. Johnson Space Center, Houston, Texas.Google Scholar
  67. Norman, D. (1988). The design of everyday things.Google Scholar
  68. Paeslack, V. and Roesler, H. (1977). Design and control of a manipulator for tetraplegics. Mech. Machine Theory, 12:413–423.CrossRefGoogle Scholar
  69. Phillips, B. and Chao, (1994).Google Scholar
  70. Pieper, S., Rosen, J., and Zeltzer, D. (1992). Interactive graphics for plastic surgery: a task-level analysis and implementation. In Proceedings of the 1992 Symposium on Interactive 3D Graphics, Zeltzer, D., Catmull, E., and Levoy, M. (eds.), pp. 127–134. New York.CrossRefGoogle Scholar
  71. Plettenburg, D.H. (1989). Electric versus pneumatic power in hand prostheses for children. J. Med. Eng. Technol., 13:124–128.PubMedCrossRefGoogle Scholar
  72. Prior, S.D., Warner, P.R., White, A.S., Parsons, J.T., and Gill, R. (1993). Actuators for rehabilitation robots. Mechatronics, 3:285–294.CrossRefGoogle Scholar
  73. Radcliffe, C.W. (1960). Human engineering: mechanisms for amputees. Machine Design, 32:24–28.Google Scholar
  74. Rahman, T. et al. (1996). Task priorities and design of an arm orthosis. Technol. Disabil., 5:197–204.CrossRefGoogle Scholar
  75. Ralston, H.J. (1953). Mechanics of voluntary muscle. Amer. J. Phys. Med., 32:166–184.PubMedGoogle Scholar
  76. Reddy, N.P., Sukthankar, S.M., and Gupta, V. (1994). Virtual reality in rehabilitation. IEEE-EMBS Workshop on Rehabil. Eng. Baltimore.Google Scholar
  77. Rosen, J.M. (1994). VR and surgery: from simulation to performing complex procedures. Virtual Reality and Medicine- The Cutting Edge. New York.Google Scholar
  78. Sanders, M.S. and McCormick, E.J. (1993). Human factors in Engineering and Design. McGraw-Hill, New York.Google Scholar
  79. Schulte, R.A. (1961). The characteristics of the McKibben artificial muscle. In Application of External Power in Prosthetics and Orthotics, Publ. 874, NASRC, pp. 94–115.Google Scholar
  80. Schuyler, J. and Mahoney, R. (1995). Vocational robotics: job identification and analysis. RESNA Proc., 15:542–544. Vancouver.Google Scholar
  81. Seamone, W. and Schmeisser, G. (1985). Early clinical evaluation of a robot arm/worktable system for spinal-cord-injured persons. J. Rehabil. Res. Dev., pp. 38–57.Google Scholar
  82. Sheredos, S.J. et al. (1996). Preliminary evaluation of the helping hand electro-mechanical arm. Technol. Disabil., 5:229–232.CrossRefGoogle Scholar
  83. Sheridan, T.B. and Ferrell, W.R. Man-Machine Systems: Information, Control, and Decision Models of Human Performance. The MIT Press, Cambridge.Google Scholar
  84. Simpson, D.C. (1974). The choice of control system for the multimovement prosthesis: extended physiological proprioception (e.p.p.). The Control of Upper Extremity Prostheses and Orthoses. Herberts, P. et al. (eds.), pp. 146–150. Charles C. Thomas, New York.Google Scholar
  85. Simpson, D.C. and Smith, J.G. (1977). An externally powered controlled complete arm prosthesis. J. Med. Eng. Tech., pp. 275–277.Google Scholar
  86. Stark, L. (1968). Neurological Control Systems. Plenum Press, New York.Google Scholar
  87. Stark, L. and Ellis, S.R. (1983). Scanpaths revisited: cognitive models direct active looking. In Eye Movements: Congnition and Visual Perception. Fisher, D.F. et al. (eds.). Lawrence Erbaum Assoc.Google Scholar
  88. Stroud, S. et al. (1996). A body powered rehabilitation robot. RESNA Proc., 16:363–365. Salt Lake City.Google Scholar
  89. Suh, C.H. and Radcliffe, C.W. (1967). Synthesis of spherical linkages with use of the displacment matrix. J. Eng. Ind., 89:215–222.Google Scholar
  90. Sukthankar, S.M. (1996). Virtual reality in rehabilitation, RESNA 1996 Mid-Atlantic Regional Conference. Philadelphia.Google Scholar
  91. Topping, M. (1996). Handy I, a robotic aid to independence for severely disabled people. Tech. Disabil., 5:233–234.CrossRefGoogle Scholar
  92. Upton, C. (1994). The RAID workstation. Rehab. Robotics Newsletter, A.I. duPont Institute, 6(1).Google Scholar
  93. Van der Loos, H.F.M. (1995). VA/Stanford rehabilitation robotics research and development program: lessons learned in the application of robotics technology to the field of rehabilitation. IEEE Trans. Rehab. Eng., 3:46–55.CrossRefGoogle Scholar
  94. Verburg, G. et al. (1996). Manus: the evolution of an assistive technology. Technol. Disabil., 5:217–228.CrossRefGoogle Scholar
  95. Virtual Technologies (1995). CyberGlove User’s Manual.Google Scholar
  96. Weghorst, S., Prothero, J., and Furness, T. (1994). Virtual images in the treatment of Parkinson’s Disease akinesia. Medicine meets Virtual Reality II: Visionary Applications for Simulation, Visualization, and Robotics. pp. 244–246. Aligned Management Associates, San Diego.Google Scholar
  97. Welford, A.T. (1976). Ergonomics: where have we been and where are we going: I. Ergonomics, 19(3):275–286.PubMedCrossRefGoogle Scholar
  98. Werbos, P. (1990). A menu of designs for reinforcement learning over time. In Neural Networks for Control. Miller et al., (eds.). The MIT Press.Google Scholar
  99. Wickens, C.D., and Baker, P. (1995). Cognitive issues in virtual reality. In Virtual Environments and Advanced Interface Design. Barfield, W. and Furness, T.A. III (eds.), pp. 514–541. Oxford University Press, New York.Google Scholar
  100. Winters, J.M. (1995a). How detailed should muscle models be to understand multi-joint movement coordination? Human Mov. Sci., 14:401–442.CrossRefGoogle Scholar
  101. Winters, J.M. (1995b). An improved muscle-reflex actuator for use in large-scale neuromusculoskeletal models. Annals Biomed. Eng., 23:359–374.CrossRefGoogle Scholar
  102. Winters, J.M. (1996). Intelligent synthesis of neuromusculoskeletal signals using fuzzy expert critics. SPIE Smart Sensing, Processing and Instrumentation: Smart Sensors and Actuators for Neural Prosthesis, Vol. 2718, pp. 456–468, San Diego.Google Scholar
  103. Winters, J.M. and Sagranichiny, E.S. (1994). Why braided pneumatic actuators in rehabilitation robotics? Principles, properties and suggested applications. 4 th Int. Conf. Rehab. Robotics, pp. 201–208, Wilmington.Google Scholar
  104. Young, L.R. and Stark, L. (1963). Variable feedback experiments testing a sampled data model for eye tracking movements. IEEE Trans. Human Factors Elec., HFE-4:38–51.CrossRefGoogle Scholar
  105. Zadah, L.A. (1994). Fuzzy logic, neural networks, and soft computing. Commun. ACM 37:77–84.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York, Inc. 2000

Authors and Affiliations

  • Jack M. Winters
  • Corinna Lathan
  • Sujat Sukthankar
  • Tanja M. Pieters
  • Tariq Rahman

There are no affiliations available

Personalised recommendations