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Beyond Anthropometrics: Prehensile Control Analysis for Capability Assessment

Conference paper
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Abstract

How can we design objects that are better suited to people with disabilities? Finite Element Analysis is a useful technique for engineering physical objects, but optimal design must be informed by how the human interacts with objects. Our group is attempting to model the control of hand movements in order to create CAD packages that allow object design to be informed by an individual’s sensorimotor control strategies (Prehensile Control Analysis). Prehension, the ability to reach-grasp-and-manipulate objects, is one of the most important human capabilities. Numerous activities of daily living (dressing, feeding, cleaning etc.) rely on dexterity, so it is perhaps unsurprising that impairment of prehension (through illness, injury or ageing decline) is often associated with disability. The kinematics of reach-to-grasp movements show high levels of stereotypicality in neurologically intact adults whilst impairment produces predictable kinematic changes in behaviour. Moreover, kinematics change lawfully as a function of the task and the properties of the object. These facts open up the exciting possibility of modelling prehensile kinematics so that a designer can determine the optimal object properties for an individual with a given impairment. This chapter presents a simple model for characterising an individual’s quality of movement in a given reach-to-grasp movement. Our model is able to capture typical and atypical prehension and is the first step in the development of CAD for handheld objects: a tool that allows design around people.

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References

  1. Arimoto S, Sekimoto M (2006) Human-like movements of robotic arms with redundant DOFs: Virtual spring-damper hypothesis to tackle the Bernstein problem. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation, pp. 1860-66, Orlando, FL, USAGoogle Scholar
  2. Dreyfus H (1967) The measure of man: Human factors in design, 2nd Ed. WhitneyGoogle Scholar
  3. Feldman AG (1986) Once more on the equilibrium-point hypothesis (λ model) for motor control. Journal of Motor Behavior 18(1): 17-54Google Scholar
  4. Flatters IJ, Otten L, Witvliet A, Henson B, Holt RJ, Culmer P et al. (2012) Predicting the effect of surface texture on the qualitative form of prehension. PLoS ONE 7(3): e32770Google Scholar
  5. Gonzalez V, Rowson J, Yoxall A (2015) Development of the variable dexterity test: Construction, reliability and validity. International Journal of Therapy and Rehabilitation 22 (4): 174-180Google Scholar
  6. Holt RJ, Lefevre AS, Flatters IJ, Culmer P, Wilkie RM, Henson BW et al. (2013) Grasping the changes seen in older adults when reaching for objects of varied texture. PLoS ONE 8(7): e69040Google Scholar
  7. Jeannerod M (1988) The neural and behavioural organization of goal-directed movements. Clarendon Press/Oxford University PressGoogle Scholar
  8. Light CM, Chappell PH, Kyberd PJ (2002) Establishing a standardized clinical assessment tool of pathologic and prosthetic hand function: Normative data, reliability, and validity. Archives of Physical Medicine and Rehabilitation 83(6): 776-783Google Scholar
  9. Mathiowetz V, Volland G, Kashman N, Weber K (1985) Adult norms for the Box and Block Test of manual dexterity. American Journal of Occupational Therapy 39(6): 386-391Google Scholar
  10. Mizhari J (2015) Mechanical impedance and its relations to motor control, limb dynamics and motion biomechanics. Journal of Medical and Biological Engineering 3(1): 1-20Google Scholar
  11. Mon-Williams M, Bingham GP (2011) Discovering affordances that determine the spatial structureof reach-to-grasp movements. Experimental Brain Research 211(1): 145-160Google Scholar
  12. Porter JM, Case K, Marshall R, Gyi DE, Sims R (2004) Beyond Jack and Jill: Designing for individuals using HADRIAN. International Journal of Industrial Ergonomics 33(3): 249-264Google Scholar
  13. Smith SA, Norris BJ, Peebles L (2000) OLDER ADULTDATA: The handbook of measurements and capabilities of the older adult – Data for design safety. Department of Trade and Industry, London, UKGoogle Scholar
  14. Tiffin J, Asher EJ (1948) The Purdue Pegboard: Norms and studies of reliability and validity. Journal of Applied Psychology 32(3): 234Google Scholar
  15. Yoxall A, Luxmoore J, Austin M, Canty L, Margrave KJ, Richardson CJ et al. (2007) Getting to grips with packaging: Using ethnography and computer simulation to understand hand–pack interaction. Packaging Technology and Science 20: 217-229Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Mechanical EngineeringUniversity of LeedsLeedsUK
  2. 2.School of PsychologyUniversity of LeedsLeedsUK
  3. 3.Department of Psychological and Brain SciencesIndiana UniversityINUSA

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