Abstract
It is amazing for human to control highly complex hand with many degrees of freedom. To explore the mystery of hand, we use correlation analysis on human hand movement dataset, which is recorded from 33 kinds of grasping tasks in daily life, and obtain correlation relationships of all joints by hierarchical cluster analysis. The correlation relationships imply the feature of human hand movement. Thumb move relatively independently and other fingers move relatively synergistically during all grasping tasks. Moreover, DIP and PIP joints of all four fingers connect closer together than MCP joints. Before that work in this paper, we try to use dimensional reduction method, which is the main technique, to study the synergistic characteristic. It also supports the conclusion by the considerable inhomogeneity of index of RREV, which is raised to assess the error of each joint variable.
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References
Soechting, J.F., Flanders, M.: Flexibility and repeatability of finger movements during typing: Analysis of multiple degrees of freedom. Journal of Computational Neuroscience 4, 29–46 (1997)
Santello, M., Flanders, M., Soechting, J.F.: Postural hand synergies for tool use. The Journal of Neuroscience 18, 10105–10115 (1998)
Turvey, M.T.: Action and perception at the level of synergies. Human Movement Science 26, 657–697 (2007)
Santello, M., Baud-Bovy, G., Jörntell, H.: Neural bases of hand synergies. Frontiers in Computational Neuroscience 7 (2013)
Feix, T., Pawlik, R., Schmiedmayer, H., Romero, J., Kragic, D.: A comprehensive grasp taxonomy. In: Robotics, Science and Systems: Workshop on Understanding the Human Hand for Advancing Robotic Manipulation, pp. 2–3 (2009)
Cutkosky, M.R.: On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Transactions on Robotics and Automation 5, 269–279 (1989)
Tresch, M.C., Cheung, V.C., D’Avella, A.: Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets. Journal of Neurophysiology 95, 2199–2212 (2006)
Ciocarlie, M., Goldfeder, C., Allen, P.: Dimensionality reduction for hand-independent dexterous robotic grasping. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007, San Diego, CA, pp. 3270–3275 (2007)
Ingram, J.N., Körding, K.P., Howard, I.S., Wolpert, D.M.: The statistics of natural hand movements. Experimental Brain Research 188, 223–236 (2008)
Häger-Ross, C., Schieber, M.H.: Quantifying the independence of human finger movements: comparisons of digits, hands, and movement frequencies. The Journal of Neuroscience 20, 8542–8550 (2000)
Liu, H., Wu, K., Meusel, P., Seitz, N., Hirzinger, G., Jin, M.H., Liu, Y.W., Fan, S.W., Lan, T., Chen, Z.P.: Multisensory five-finger dexterous hand: The DLR/HIT Hand II. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, pp. 3692–3697 (2008)
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Liu, M., Xiong, C. (2014). Synergistic Characteristic of Human Hand during Grasping Tasks in Daily Life. In: Zhang, X., Liu, H., Chen, Z., Wang, N. (eds) Intelligent Robotics and Applications. ICIRA 2014. Lecture Notes in Computer Science(), vol 8917. Springer, Cham. https://doi.org/10.1007/978-3-319-13966-1_7
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DOI: https://doi.org/10.1007/978-3-319-13966-1_7
Publisher Name: Springer, Cham
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