Abstract
Stroke is the leading cause of permanent adult disability in the U.S., frequently resulting in chronic motor impairments. Rehabilitation of the upper limb, particularly the hand, is especially important as arm and hand deficits post-stroke limit the performance of activities of daily living and, subsequently, functional independence. Hand rehabilitation is challenging due to the complexity of motor control of the hand. New instrumentation is needed to facilitate examination of the hand. Thus, a novel actuated exoskeleton for the index finger, the FingerBot, was developed to permit the study of finger kinetics and kinematics under a variety of conditions. Two such novel environments, one applying a spring-like extension torque proportional to angular displacement at each finger joint and another applying a constant extension torque at each joint, were compared in 10 stroke survivors with the FingerBot. Subjects attempted to reach targets located throughout the finger workspace. The constant extension torque assistance resulted in a greater workspace area (p < 0.02) and a larger active range of motion for the metacarpophalangeal joint (p < 0.01) than the spring-like assistance. Additionally, accuracy in terms of reaching the target was greater with the constant extension assistance as compared to no assistance. The FingerBot can be a valuable tool in assessing various hand rehabilitation paradigms following stroke.
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Barber, C. B., D. P. Dobkin, and H. T. Huhdanpaa. The Quickhull algorithm for convex hulls. ACM Trans. Math. Software 22:469–483, 1996.
Beer, R. F., J. P. Dewald, M. L. Dawson, and W. Z. Rymer. Target-dependent differences between free and constrained arm movements in chronic hemiparesis. Exp. Brain Res. 156:458–470, 2004.
Canning, C. G., L. Ada, R. Adams, and N. J. O’Dwyer. Loss of strength contributes more to physical disability after stroke than loss of dexterity. Clin. Rehabil. 18:300–308, 2004.
Canning, C. G., L. Ada, and N. J. O’Dwyer. Abnormal muscle activation characteristics associated with loss of dexterity after stroke. J. Neurol. Sci. 176:45–56, 2000.
Chae, J., G. Yang, B. K. Park, and I. Labatia. Muscle weakness and cocontraction in upper limb hemiparesis: relationship to motor impairment and physical disability. Neurorehabil. Neural Repair 16:241–248, 2002.
Cruz, E. G., and D. G. Kamper. Kinematics of point-to-point finger movements. Exp. Brain Res. 174:29–34, 2006.
Cruz, E. G., H. C. Waldinger, and D. G. Kamper. Kinetic and kinematic workspaces of the index finger following stroke. Brain 128:1112–1121, 2005.
Darling, W. G., and K. J. Cole. Muscle activation patterns and kinetics of human index finger movements. J. Neurophysiol. 63:1098–1108, 1990.
Jack, D., R. Boian, A. S. Merians, M. Tremaine, G. C. Burdea, S. V. Adamovich, M. Recce, and H. Poizner. Virtual reality-enhanced stroke rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 9:308–318, 2001.
Kahn, L. E., P. S. Lum, W. Z. Rymer, and D. J. Reinkensmeyer. Robot-assisted movement training for the stroke-impaired arm: does it matter what the robot does? J. Rehabil. Res. Dev. 43:619–630, 2006.
Kamper, D. G., E. G. Cruz, and M. P. Siegel. Stereotypical fingertip trajectories during grasp. J. Neurophysiol. 90:3702–3710, 2003.
Kamper, D. G., H. C. Fischer, E. G. Cruz, and W. Z. Rymer. Weakness is the primary contributor to finger impairment in chronic stroke. Arch. Phys. Med. Rehabil. 87:1262–1269, 2006.
Kamper, D. G., and W. Z. Rymer. Quantitative features of the stretch response of extrinsic finger muscles in hemiparetic stroke. Muscle Nerve 23:954–961, 2000.
Lang, C. E., and M. H. Schieber. Human finger independence: limitations due to passive mechanical coupling versus active neuromuscular control. J. Neurophysiol. 92:2802–2810, 2004.
Li, Z. M., G. Davis, N. P. Gustafson, and R. J. Goitz. A robot-assisted study of intrinsic muscle regulation on proximal interphalangeal joint stiffness by varying metacarpophalangeal joint position. J. Orthop. Res. 24:407–415, 2006.
Mali, U., N. Goljar, and M. Munih. Application of haptic interface for finger exercise. IEEE Trans. Neural Syst. Rehabil. Eng. 14:352–360, 2006.
Mallon, W. J., H. R. Brown, and J. A. Nunley. Digital ranges of motion: normal values in young adults. J. Hand. Surg. [Am.] 16:882–887, 1991.
Patten, C., J. Lexell, and H. E. Brown. Weakness and strength training in persons with poststroke hemiplegia: rationale, method, and efficacy. J. Rehabil. Res. Dev. 41:293–312, 2004.
Patton, J. L., M. E. Stoykov, M. Kovic, and F. A. Mussa-Ivaldi. Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors. Exp. Brain Res. 168:368–383, 2006.
Powers, R. K., J. Marder-Meyer, and W. Z. Rymer. Quantitative relations between hypertonia and stretch reflex threshold in spastic hemiparesis. Ann. Neurol. 23:115–124, 1988.
Rosamond, W., K. Flegal, G. Friday, K. Furie, A. Go, K. Greenlund, N. Haase, M. Ho, V. Howard, B. Kissela, S. Kittner, D. Lloyd-Jones, M. McDermott, J. Meigs, C. Moy, G. Nichol, C. J. O’Donnell, V. Roger, J. Rumsfeld, P. Sorlie, J. Steinberger, T. Thom, S. Wasserthiel-Smoller, and Y. Hong. Heart disease and stroke statistics—2007 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 115:e69–e171, 2007.
Sanchez, R. J., J. Liu, S. Rao, P. Shah, R. Smith, T. Rahman, S. C. Cramer, J. E. Bobrow, and D. J. Reinkensmeyer. Automating arm movement training following severe stroke: functional exercises with quantitative feedback in a gravity-reduced environment. IEEE Trans. Neural Syst. Rehabil. Eng. 14:378–389, 2006.
Spong, M. W., S. Hutchinson, and M. Vidyasagar. Robot Modeling and Control. London: John Wiley & Sons, Inc, 2006.
Takahashi, C. D., L. Der-Yeghiaian, V. Le, R. R. Motiwala, and S. C. Cramer. Robot-based hand motor therapy after stroke. Brain 131:425–437, 2008.
Thom, T., N. Haase, W. Rosamond, V. J. Howard, J. Rumsfeld, T. Manolio, Z. J. Zheng, K. Flegal, C. O’Donnell, S. Kittner, D. Lloyd-Jones, D. C. Goff, Jr., Y. Hong, R. Adams, G. Friday, K. Furie, P. Gorelick, B. Kissela, J. Marler, J. Meigs, V. Roger, S. Sidney, P. Sorlie, J. Steinberger, S. Wasserthiel-Smoller, M. Wilson, and P. Wolf. Heart disease and stroke statistics—2006 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 113:e85–e151, 2006.
Thoroughman, K. A., and R. Shadmehr. Learning of action through adaptive combination of motor primitives. Nature 407:742–747, 2000.
Trombly, C. Occupational Therapy for Physical Dysfunction. Baltimore: Williams and Wilkins, 1989.
Wade, D. The Epidemiologically Based Needs Assessment Reviews, Vol. I. Oxford: Radcliffe Medical Press, pp. 111–255, 1994.
Wege, A., and A. Zimmerman. Electromyography sensor based control for a hand exoskeleton. In: IEEE International Conference on Robotics and Biomimetics, Sanya, China, 2007, pp. 1470–1475.
Wolbrecht, E. T., V. Chan, D. J. Reinkensmeyer, and J. E. Bobrow. Optimizing compliant, model-based robotic assistance to promote neurorehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 16:286–297, 2008.
Acknowledgments
This work was supported by a grant from the Coleman Foundation. We wish to thank Dr. Edward Colgate and Dr. Eric Perreault for their insightful recommendations regarding the design of the FingerBot and of the experiment and Mr. Dan Qiu for his examination of the sensitivity of the Jacobian matrix.
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Appendix
Appendix
The Jacobian matrix (J) relating the three joint torques \( \left( {{\varvec{\tau}} = \left[ {\begin{array}{*{20}c} {{\varvec{\tau}}_{\mathbf{MCP}} } \\ {{\varvec{\tau}}_{\mathbf{PIP}} } \\ {{\varvec{\tau}}_{\mathbf{DIP}} } \\ \end{array} } \right]} \right) \) to the forces and moment \( \left( {{\mathbf{f}} = \left[ {\begin{array}{*{20}c} {{\mathbf{F}}_{\mathbf{x}} } \\ {{\mathbf{F}}_{\mathbf{y}} } \\ {{\varvec{\tau}}_{\mathbf{z}} } \\ \end{array} } \right]} \right) \) at the fingertip is given by Eq. A.1:
where, l P is the length of proximal finger segment, l M is the length of middle finger segment, l D is the length of distal finger segment, θP is the MCP flexion/extension angle, θM is the PIP flexion/extension angle, and θD is the DIP flexion/extension angle.
The joint torques are related to the fingertip forces/moment by:
Changes in the angle with respect to the fingertip of an applied force affects the joint torques generated. The changes are not uniform across the joints. For example, altering the force direction from 80° with respect to the long axis of the distal segment to 100° has minimal effect on the PIP and DIP torques, but has a significant effect on the MCP torque (Fig. A.1).
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Cruz, E.G., Kamper, D.G. Use of a Novel Robotic Interface to Study Finger Motor Control. Ann Biomed Eng 38, 259–268 (2010). https://doi.org/10.1007/s10439-009-9845-4
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DOI: https://doi.org/10.1007/s10439-009-9845-4