Abstract
This paper reports the results of two studies carried out in a controlled environment aiming to understand relationships between movement patterns of coordination that emerge during climbing and performance outcomes. It involves a recent method of nonlinear dimensionality reduction, multi-scale Jensen–Shannon neighbor embedding (Lee et al., 2015), which has been applied to recordings of movement sensors in order to visualize coordination patterns adapted by climbers. Initial clustering at the climb scale provides details linking behavioral patterns with climbing fluency/smoothness (i.e., the performance outcome). Further clustering on shorter time intervals, where individual actions within a climb are analyzed, enables more detailed exploratory data analysis of behavior. Results suggest that the nature of individual learning curves (the global, trial-to-trial performance) corresponded to certain behavioral patterns (the within trial motor behavior). We highlight and discuss three distinctive learning curves and their corresponding relationship to behavioral pattern emergence, namely: no improvement and a lack of new motor behavior emergence; sudden improvement and the emergence of new motor behaviors; and gradual improvement and a lack of new motor behavior emergence.
Similar content being viewed by others
References
Bardy B, Oullier O, Bootsma RJ, Stoffregen TA (2002) Dynamics of human postural transitions. J Exp Psychol Hum Percept Perform 28(3):499
Basseville M, Nikiforov IV et al (1993) Detection of abrupt changes: theory and application, vol 104. Prentice Hall, Englewood Cliffs
Bernstein NA, Latash ML, Turvey M (1996) Dexterity and its development. Taylor & Francis, New York
Boulanger J, Seifert L, Herault R, Coeurjolly JF (2016) Automatic sensor-based detection and classification of climbing activities. IEEE Sens J 16(3):742–749. doi:10.1109/JSEN.2015.2481511
Chow JY, Davids K, Button C, Koh M (2008) Coordination changes in a discrete multi-articular action as a function of practice. Acta Psychol 127(1):163–176
Cordier P, France MM, Bolon P, Pailhous J (1993) Entropy, degrees of freedom, and free climbing: a thermodynamic study of a complex behavior based on trajectory analysis. Int J Sport Psychol 24(4):370–378
Davids K, Button C, Araújo D, Renshaw I, Hristovski R (2006) Movement models from sports provide representative task constraints for studying adaptive behavior in human movement systems. Adapt Behav 14(1):73–95
Demartines P, Hérault J (1997) Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets. IEEE Trans Neural Netw 8(1):148–154
Engø K (2001) On the BCH-formula in so (3). BIT Numer Math 41(3):629–632
Gel’fand IM, Tsetlin M (1962) Some methods of control for complex systems. Russ Math Sur 17(1):95
Hall B (2015) Lie groups, Lie algebras, and representations: an elementary introduction. Springer, Berlin
Hinton GE, Roweis ST (2002) Stochastic neighbor embedding. In: Advances in neural information processing systems, pp 833–840
Kelso J (1984) Phase transitions and critical behavior in human bimanual coordination. Am J Physiol Regul Integr Comp Physiol 246(6):R1000–R1004
Kostrubiec V, Zanone PG, Fuchs A, Kelso JS (2012) Beyond the blank slate: routes to learning new coordination patterns depend on the intrinsic dynamics of the learner—experimental evidence and theoretical model. Front Hum Neurosci 6:1–14
Kruskal J (1964) Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29:1–28
Lee J, Verleysen M (2014) Two key properties of dimensionality reduction methods. In: IEEE SSCI 2014—2014 IEEE symposium series on computational intelligence—CIDM 2014: 2014 IEEE symposium on computational intelligence and data mining, pp 163–170
Lee JA, Renard E, Bernard G, Dupont P, Verleysen M (2013) Type 1 and 2 mixtures of kullback-leibler divergences as cost functions in dimensionality reduction based on similarity preservation. Neurocomputing 112:92–108
Lee JA, Peluffo-Ordóñez DH, Verleysen M (2015) Multi-scale similarities in stochastic neighbour embedding: reducing dimensionality while preserving both local and global structure. Neurocomputing 169:246–261
Madgwick S (2010) An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Report x-io and University of Bristol, UK
Madgwick S, Harrison A, Vaidyanathan R (2011) Estimation of imu and marg orientation using a gradient descent algorithm. In: 2011 ieee international conference on rehabilitation robotics (ICORR), pp 1–7
Manton JH (2004) A globally convergent numerical algorithm for computing the centre of mass on compact lie groups. In: Control, automation, robotics and vision conference, 2004. ICARCV 2004 8th, IEEE, vol 3, pp 2211–2216
Nourrit D, Delignières D, Caillou N, Deschamps T, Lauriot B (2003) On discontinuities in motor learning: a longitudinal study of complex skill acquisition on a ski-simulator. J Mot Behav 35(2):151–170
Orth D, Davids K, Seifert L (2016) Coordination in climbing: effect of skill, practice and constraints manipulation. Sports Med 46(2):255–268
Pijpers J, Oudejans RR, Bakker FC, Beek PJ (2006) The role of anxiety in perceiving and realizing affordances. Ecol Psychol 18(3):131–161
Sammon J (1969) A nonlinear mapping algorithm for data structure analysis. IEEE Trans Comput 18(5):401–409
Seifert L, Coeurjolly JF, Hérault R, Wattebled L, Davids K (2013) Temporal dynamics of inter-limb coordination in ice climbing revealed through change-point analysis of the geodesic mean of circular data. J Appl Stat 40(11):2317–2331
Seifert L, L’Hermette M, Komar J, Orth D, Mell F, Merriaux P, Grenet P, Caritu Y, Hérault R, Dovgalecs V, Davids K (2014a) Pattern recognition in cyclic and discrete skills performance from inertial measurement units. Procedia Eng 72:196–201 (the Engineering of Sport 10)
Seifert L, Orth D, Boulanger J, Dovgalecs V, Herault R, Davids K (2014b) Climbing skill and complexity of climbing wall design: assessment of jerk as a novel indicator of performance fluency. J Appl Biomech 30(5):619–625
Seifert L, Wattebled L, Herault R, Poizat G, Adé D, Gal-Petitfaux N, Davids K (2014c) Neurobiological degeneracy and affordance perception support functional intra-individual variability of inter-limb coordination during ice climbing. PLoS ONE 9(2):e89865
Shepard R (1962) The analysis of proximities: multidimensional scaling with an unknown distance function (parts 1 and 2). Psychometrika 27(125–140):219–249
Teulier C, Delignieres D (2007) The nature of the transition between novice and skilled coordination during learning to swing. Hum Mov Sci 26(3):376–392
Torgerson W (1952) Multidimensional scaling, I: theory and method. Psychometrika 17:401–419
van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605
World Medical Association (2013) World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310(20):2191
Acknowledgements
This work was partially funded by the Agence Nationale de la Recherche with the ANR-13-JSH2-004 Dynamov Project. and Belgian F.R.S.-FNRS (National Fund of Scientific Research).
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible editors: Ulf Brefeld and Albrecht Zimmermann.
Rights and permissions
About this article
Cite this article
Herault, R., Orth, D., Seifert, L. et al. Comparing dynamics of fluency and inter-limb coordination in climbing activities using multi-scale Jensen–Shannon embedding and clustering. Data Min Knowl Disc 31, 1758–1792 (2017). https://doi.org/10.1007/s10618-017-0522-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10618-017-0522-1