Cluster analysis of movement patterns in multiarticular actions: A tutorial

Motor Control

Volumn 14, Issue 2, 2010, Pages 211-239

  Rein, Robert ^a   Button, Chris ^b   Davids, Keith ^c   Summers, Jeffery ^d  

^a ÉCOLE DES HAUTES ÉTUDES EN SCIENCES SOCIALES   (France)

^b UNIVERSITY OF OTAGO   (New Zealand)

^c QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^d UNIVERSITY OF TASMANIA   (Australia)

Author keywords

Cluster analysis; Control; Coordination; Movement pattern

Indexed keywords

ADULT; ARTICLE; BODY MOVEMENT; CLUSTER ANALYSIS; HUMAN; HUMAN EXPERIMENT; MOTOR CONTROL; MOVEMENT (PHYSIOLOGY); PHYLOGENETIC TREE; TASK PERFORMANCE;

EID: 77950811660     PISSN: 10871640     EISSN: 15432696     Source Type: Journal    
DOI: 10.1123/mcj.14.2.211     Document Type: Article

References (69)

1. Balasubramaniam, R., & Turvey, M.T. (2004). Coordination modes in the multisegmental dynamics of hula hooping. Biological Cybernetics, 90(3), 176-190. (Pubitemid 40877327)
2. Ball, K.A., & Best, R.J. (2007). Different centre of pressure patterns within the golf stroke I: Cluster analysis. Journal of Sports Sciences, 25(7), 757-770. (Pubitemid 46563311)
3. Bauer, H.U., & Schöllhorn, W. (1997). Self-organizing maps for the analysis of complex movement patterns. Neural Processing Letters, 5(3), 193-199.
4. Blashfield, R.K., & Aldenderfer, M.S. (1978). The literature on cluster analysis. Multivariate Behavioral Research, 13(3), 271-295.
5. Breckenridge, J.N. (2000). Validating cluster analysis: Consistent replication and symmetry. Multivariate Behavioral Research, 35(2), 261-285.
6. Button, C., Davids, K., & Schöllhorn, W. (2006). Coordination profiling of movement systems. In K. Davids, S. Bennett, & K. Newell (Eds.), Movement System Variability (pp. 133-152). Champaign, IL: Human Kinetics.
7. Button, C., MacLeod, M., Sanders, R., & Coleman, S. (2003). Examining movement variability in the basketball free-throw action at different skill levels. Research Quarterly for Exercise and Sport, 74(3), 257-269. (Pubitemid 44530765)
8. Chau, T. (2001). A review of analytical techniques for gait data. Part 1: fuzzy, statistical and fractal methods. Gait & Posture, 39(1), 49-66. (Pubitemid 32109219)
9. Chen, H-H., Liu, Y-T., Mayer-Kress, G., & Newell, K.M. (2005). Learning the pedalo locomotion task. Journal of Motor Behavior, 37(3), 247-256.
10. Chow, J.Y., Davids, K., Button, C., & Koh, M. (2008). Coordination changes in a discrete multi-articular action as a function of practice. Acta Psychologica, 127(1), 163-176.
11. Corder, A.F., Levin, O., Li, Y., & Swinnen, S.P. (2005). Principal component analysis of complex multijoint coordinative movements. Biological Cybernetics, 93(1), 63-78. (Pubitemid 41067712)
12. Cordero, A.F., Koopman, H.J.F.M., & van der Helm, F.C.T. (2006). Describing gait as a sequence of states. Journal of Biomechanics, 39(5), 948-957.
13. d'Avella, A., Portone, A., Fernandez, L., & Lacquaniti, F. (2006). Control of fast-reaching movements by muscle synergy combinations. The Journal of Neuroscience, 26(30), 7791-7810.
14. Daffertshofer, A., Lamoth, C.J.C., Meijer, O.G., & Beek, P.J. (2004). PCA in studying coordination and variability: a tutorial. Clinical Biomechanics (Bristol, Avon), 19(4), 415-428. (Pubitemid 38510420)
15. de Freitas, S.M.S.F., Scholz, J.P., & Stehman, A.J. (2007). Effect of motor planning on use of motor abundance. Neuroscience Letters, 417(1), 66-71.
16. Everitt, B.S., Landau, S., & Leese, M. (2001). Cluster analysis (4th ed.). London: Arnold.
17. Gan, G., Ma, C., & Jianhon, W. (2007). Data clustering: Theory, algorithms, and applications. Philadelphia: ASA SIAM.
18. Gnanadesikan, R., Kettenring, J.R., & Srinivas, M. (2007). Better alternatives to current methods of scaling and weighting data for cluster analysis. Journal of Statistical Planning and Inference, 137(1), 3483-3496.
19. Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2002a). Cluster validity methods: Part I. SIGMOD Record, 31(2), 40-45.
20. Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2002b). Clustering validity checking methods: Part II. SIGMOD Record, 31(3), 19-27.
21. Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques (2 ed.). San Francisco: Morgan Kaufmann.
22. Handl, J., Knowles, J., & Kell, D.B. (2005). Computational cluster validation in post-genomic data analysis. Bioinformatics (Oxford, England), 21(15), 3201-3212. (Pubitemid 41418432)
23. Hands, S., & Everitt, B. (1987). A monte carlo study of the recovery of cluster structure in binary data by hierarchical clustering techniques. Multivariate Behavioral Research, 22, 235-243.
24. Hong, S.L., & Newell, K.M. (2006). Practice effects on local and global dynamics of the ski-simulator task. Experimental Brain Research, 169(3), 350-360.
25. Hung, Y.C., Kaminski, T.R., Fineman, J., Monroe, J., & Gentile, A.M. (2008). Learning a multi-joint throwing task: a morphometric analysis of skill development. Experimental Brain Research, 191(2), 197-208.
26. Jain, A.K. (1988). Algorithms for clustering data. Englewood Cliffs, New Jersey: Prentice Hall.
27. Jaitner, T., Mendoza, L., & Schöllhorn, W.I. (2001). Analysis of the long jump technique in the transitions from approach to takeoff. Based on time-continuous kinematic data. European Journal of Sport Science, 1(5), 1-11.
28. Kaufmann, L., & Rousseeuw, P.J. (1990). Finding groups in data: An introduction to cluster analysis. New York: John Wiley & Sons, Inc.
29. Kelso, J.A.S. (1995). Dynamic patterns: The self-organization of brain and behavior. Cambridge, Mass.: MIT Press.
30. Kelso, J.A.S., & Schöner, G. (1988). Self-organization of coordinative movement patterns. Human Movement Science, 7(1), 27-46.
31. Ketchen, D.J., Jr., & Shook, C.L. (1996). The application of cluster analysis in strategic management research: an analysis and critique. Strategic Management Journal, 17(6), 441-458.
32. Kettenring, J.R. (2006). The practice of cluster analysis. Journal of Classification, 23(1), 3-30.
33. Kuiper, F.K., & Fisher, L. (1975). A monte carlo comparison of six clustering procedures. Biometrics, 31, 777-783.
34. Lames, M. (1992). Synergetik als Konzept in der Sportmotorik. Sportpsychologie, 3, 12-18.
35. Latash, M., Danion, F., Scholz, J.F., Zatsiorsky, V.Z., & Schöner, G. (2003). Approaches to analysis of handwriting as a task of coordinating a redundant motor system. Human Movement Science, 22(2), 153-171. (Pubitemid 36356105)
36. Latash, M.L., Scholz, J.P., & Schöner, G. (2007). Toward a new theory of motor synergies. Motor Control, 11(3), 276-308.
37. Magill, R.A. (1989). Motor learning: concepts and application (3rd ed.). Dubuque, Iowa: Wm. C. Brown Publishers.
38. Martin, T.S. (1992). The art of shooting baskets: from the free throw to the slam dunk. Chicago: Contemporary Books.
39. Milligan, G.W. (1981). A review of monte carlo tests of cluster analyis. Multivariate Behavioral Research, 16(3), 379-407.
40. Mullineaux, D.R., Bartlett, R.M., & Bennett, S. (2001). Research design and statistics in biomechanics and motor control. Journal of Sports Sciences, 19(10), 739-760. (Pubitemid 32825576)
41. O'Byrne, J.M., Jenkinson, A., & O'Brien, T.M. (1998). Quantitative analysis and classification of gait patterns in cerebral palsy using a three-dimensional motion analyzer. Journal of Child Neurology, 13(3), 101-108. (Pubitemid 28185794)
42. Post, A.A., Daffertshofer, A., & Beek, P.J. (2000). Principal components in three-ball cascade juggling. Biological Cybernetics, 82(2), 143-152.
43. R-Development-Core-Team. (2006). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
44. Rencher, A.C. (2001). Methods of multivariate analysis. Wiley-Interscience.
45. Scheibler, D., & Schneider, W. (1985). Monte carlo tests of the accuracy of cluster analysis algorithms: A comparison of hierarchical and nonhierchical methods. Multivariate Behavioral Research, 20, 283-304.
46. Schmidt, O.A., Schöllhorn, W.I., & Bauer, H.U. (1997). Gait pattern analysis using self-organizing neural network. Gait & Posture, 6(3), 268-269. (Pubitemid 27499780)
47. Schöllhorn, W. (1998). Systemdynamische Betrachtung komplexer Bewegungsmuster im Lernprozess. Frankfurt am Main: Lang.
48. Schöllhorn, W. (2003). Coordination dynamics and its consequences on sports. International Journal of Computer Science in Sport, 2(2), 40-46.
49. Schöllhorn, W., Nigg, B.M., Stefanshyn, D.J., & Liu, W. (2002). Identification of individual walking patterns using time discrete and time continuous data sets. Gait & Posture, 15(2), 180-186. (Pubitemid 34175156)
50. Schöllhorn, W., Stefanyshyn, D.J., Nigg, B.M., & Liu, W. (1999). Recognition of individual walking patterns by means of artificial neural nets. Gait & Posture, 10(1), 86.
51. Scholz, J.P., & Schöner, G. (1999). The uncontrolled manifold concept: identifying control variables for a functional task. Experimental Brain Research, 126(3), 289-306.
52. Schöner, G., & Scholz, J.P. (2007). Analyzing multi-degree-of-freedom movement system based on variance: Uncovering structure vs. extracting correlations. Motor Control, 11(3), 259-275.
53. Schorer, J., Fath, F., Joseph, B., & Jaitner, T. (2007). Identification of interindividual and intraindividual movement patterns in handball players of varying expertise levels. Journal of Motor Behavior, 39(5), 409-421. (Pubitemid 350194398)
54. Shimodaira, H. (2002). An approximately unbiased test of phylogenetic tree selection. Systematic Biology, 51(3), 492-508. (Pubitemid 35180700)
55. Shimodaira, H. (2004). Approximately unbiased tests of regions using multistep-multiscale bootstrap resampling. Annals of Statistics, 32(6), 2616-2641.
56. Shimodaira, H. (2008). Testing regions with nonsmooth boundaries via multiscale bootstrap. Journal of Statistical Planning and Inference, 138(5), 1227-1241.
57. Sidaway, B., Heise, G., & Schoenfelder-Zohdi, B. (1995). Quantifying the variability of angle-angle plots. Journal of Human Movement Studies, 29, 181-197.
58. Southard, D. (1998). Mass and velocity: control parameters for throwing patterns. Research Quarterly for Exercise and Sport, 69(1), 355-359.
59. Southard, D. (2002). Change in throwing pattern: Critical values for control parameter of velocity. Research Quarterly for Exercise and Sport, 73(4), 396-407.
60. Suzuki, R., & Shimodaira, H. (2004). An application of multiscale bootstrap resampling to hierarchical clustering of microarry data: How accurate are these clusters? Paper presented at the The Fifteenth International Conference on Genome Informatics (GIW 2004), Yokohama.
61. Suzuki, R., & Shimodaira, H. (2006a). Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics (Oxford, England), 22(12), 1540-1542. (Pubitemid 43985330)
62. Suzuki, R., & Shimodaira, H. (2006b). pvclust: Hierarchical clustering with p-values via multiscale bootstrap resampling (Version 1.2-0) [R package ].
63. Theodoridis, S., & Koutroumbas, K. (2006). Pattern recognition (3 ed.). San Diego: Academic Press.
64. Toro, B., Nester, C.J., & Farren, P.C. (2007). Cluster analysis for the extraction of sagittal gait patterns in children with cerebral palsy. Gait & Posture, 25(2), 157-165. (Pubitemid 46038103)
65. Ward, J.J.H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(103), 236-244.
66. Wei, Z., Scholz, J.P., Zatsiorsky, V.M., & Latash, M. (2008). What do synergies do? Effects of secondary constraints on multidigit synergies in accurate force-production tasks. Journal of Neurophysiology, 99, 500-513.
67. Wilson, B.D., & Howard, A. (1983). The use of cluster analysis in movement description and classification of the backstroke swim start. Paper presented at the Biomechanics VIII: Proceeding of the Eight International Congress of Biomechanics, Nagoya, Japan.
68. Yang, J-F., Scholz, J.P., & Latash, M.L. (2007). The role of kinematic redundancy in adaptation of reaching. Experimental Brain Research, 176(1), 54-69.
69. Zhao, H., Liang, J., & Hu, H. (2006). Clustering validity based on the improved Hubert Gamma statistic and the separation of clusters. Paper presented at the ICICIC '06: Proceedings of the First International Conference on Innovative Computing, Information and Control, Beijing.