Abstract
Owing to the flexibility and redundancy of neuromuscular and skeletal systems, humans can trace the same hand trajectory in space with various arm configurations. However, the joint trajectories of typical unrestrained movements tend to be consistent both within and across subjects. In this paper we propose a method to solve the 3-D inverse kinematics problem based on minimizing the magnitude of total work done by joint torques. We examined the fit of the joint-space trajectories against those observed from human performance in a variety of movement paths in 3-D workspace. The results showed that the joint-space trajectories produced by the method are in good agreement with the subjects’ arm movements (r 2>0.98), with the exception of shoulder adduction/abduction (where, in the worst case, r 2 ∼0.8). Comparison of humeral rotation predicted by our algorithm with other models showed that the correlation coefficient (r 2) between actual data and our predictions is extremely high (mostly >0.98, 11 out of 15 cases, with a few exceptions, 4 of 15, in the range of 0.8–0.9) and the slope of linear regression is much closer to one (<0.05 distortion in 12 out of 15 cases, with only one case >0.15). However, the discrepancy in shoulder adduction/abduction indicated that when only the hand path is known, additional constraint(s) may be required to generate a complete match with human performance.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Admiraal MA, Kusters MJ, Gielen SC (2004) Modeling kinematics and dynamics of human arm movements. Motor Control 8(3):312–338
Augurelle AS, Smith AM, Lejeune T, Thonnard JL (2003) Importance of cutaneous feedback in maintaining a secure grip during manipulation of hand-held objects. J Neurophysiol 89(2):665–671
Carhart M (2000) Biomechanical analysis of compensatory stepping : implications for paraplegics standing via FNS. PhD Thesis Arizona State University
Flanagan JR, Nakano E, Imamuzi H, Osu R, Yoshioka T, and Kawato M (1999). Composition and decomposition of internal models in motor learning under altered kinematic and dynamic environments. J Neuroscience 19:RC34
Gielen CCAM, Vrijenhoek EJ, Flash T, Neggers SFW (1997) Arm position constraints during pointing and reaching in 3-d space. J Neurophysiol 78:660–673
Gréa H, Desmurget M, Prablanc C (2000) Postural invariance in three-dimensional reaching and grasping movements. Exp Brain Res 134:155–162
Hore J, Watts S, Vilis T (1992) Constraints on arm position when pointing in three dimensions: Donders’ law and the Fick gimbal strategy. J Neurophysiol 68:374–383
Kane TR, Levinson DA (1996) Dynamics Online: theory and implementation with AUTOLEV, Online Dynamics, Inc
Koga Y, Kondo K, Kuffner J, Latombe J-C (1994) Planning motions with intentions. In: Proceedings of the 21st annual conference on Computer graphics and interactive techniques, pp 395–408
Lacquaniti F, Soechting JF (1982) Coordination of arm and wrist motion during a reaching task. J Neurosci 2:399–408
Mamassian P (1997) Prehension of objects oriented in three-dimensional space. Exp Brain Res 114:235–245
MotCo. Geometrical and mass-inertial characteristics of the upper human limb [online]. Accessed 24 January 2003. URL: http://motco.dir.bg/Data/MassInertial.html
Soechting JF, Flanders M (1989) Errors in pointing are due to approximations in sensorimotor transformation. J Neurophysiol 62:595–608
Soechting JF, Terzuolo CA (1986) An algorithm for the generation of curvilinear wrist motion in an arbitrary plane in three dimensional space. Neuroscience 19:1393–1405
Soechting JF, Buneo CA, Herrmann U, Flanders M (1995) Moving effortlessly in three dimensions: does Donders’ law apply to arm movement? J Neurosci 15:6271–6280
Taylor DM, Tillery SIH, Schwartz AB (2002) Direct cortical control of 3D neuroprosthetic devices. Science 296:1829–1832
Tolani D, Badler N (1996) Real-time inverse kinematics of the human arm. Presence 5.4:393–401
Uno Y, Kawato M and Suzuki R (1989) Formation and control of optimal trajectory in human multi-joint arm movement. Minimum torque-change model. Biol Cybern 61:89–101
Van der Helm FCT, Chadwick EKJ (2002) A Forward-Dynamic Shoulder and Elbow Model. 4th Meeting of the International Shoulder Group, Cleveland, OH, June 17–18
Wada Y, Kaneko Y, Nakano E, Osu R, Kawato, M (2001) Quantitative examinations for multi joint arm trajectory planning–using a robust calculation algorithm of the minimum commanded torque change trajectory. Neural Netw 14(4–5):381–393
Wang XG (1999) Three-dimensional kinematic analysis of influence of hand orientation and joint limits on the control of arm postures and movements. Biol Cybern 80:449–463
Weeks DL, Sherwood DE, Noteboom JT (2002) Anticipatory modulation of precision grip force with variations in limb velocity of a curvilinear movement. J Mot Behav 34(1):59–66
Westling G, Johansson RS (1984) Factors influencing the force control during precision grip. Exp Brain Res 53(2):277–284
Yamaguchi GT (2001) Dynamic modeling of musculoskeletal motion – A Vectorized Approach for Biomechanical Analysis in Three Dimensions. In: Kluwer Academic Publishers, Norwell, MA, pp 243–250
Acknowledgements
The project is supported in part by a grant from DARPA (MDA972-00-1-0027) and by NIH contract NS-9-2321.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kang, T., He, J. & Tillery, S.I.H. Determining natural arm configuration along a reaching trajectory. Exp Brain Res 167, 352–361 (2005). https://doi.org/10.1007/s00221-005-0039-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00221-005-0039-5