1. Principal Components in Contemporary Dance Movements Kristen Hollands* 1, Alan Wing 2 & Andreas Daffertshofer 3 1 Health Maintenance & Rehabilitation, School of Health Sciences, University of Birmingham, UK Email: k.hollands@bham.ac.uk 2 Sensory Motor Neuroscience, School of Psychology, University of Birmingham, UK 3 Faculty of Human Movement Sciences, Vrije Universiteit, NL 306.16 Introduction Highly skilled movers, such as dancers, may employ kinematic synergies (coupled movements of several segments) in order to simplify the control of complex movement execution. Recently, Principal Component analysis (PCA) has been used, with good effect, in studies of human locomotion [1-4] to reduce high dimensional data sets and to identify interrelationships of temporal, kinematic and kinetic variables. The goal of this research is to investigate the utility of PCA to identify invariant or common features within contemporary dance movement patterns. Methods A 15s movement phrase of contemporary dance choreography was created and performed in silence by two dancers (S1, female & S2, male) from a professional dance company.The movement phrase was created by defining 3 self-selected reference points in space around the body and then weaving a series of movements in and around these points in such a way that both hands passed repeatedly through the reference points. The paths of the hands between points varied from curved to straight trajectories resulting in a movement phrase incorporating whole body translations and rotations. The phrase was performed 5 times under each of three different conditions (arms only, legs only & full body). The kinematics of the movements were measured using a 6 camera, Vicon 3D motion analysis system (Oxford, Metrics) with a 32 marker whole body set-up sampled at 120 Hz. Data Analysis Principal Component Analysis (PCA) PCA was applied separately for each dancer to the raw kinematics of all markers then to a subset of 5 markers. Results of these analyses (Figure 2) indicate that only 9 components are required to account for 90% of the total variance using either marker set. As a result, PCA was applied to the subset of 5 markers (R&L toes, R&L forearms & sternum) using a covariance matrix of 225 time-series, per dancer, as input for the analysis. References 1)A.Daffertshofer, C.J.C. Lamoth, O.G. Meijer, P.J. Beek (2004) PCA in studying coordination and variability: a tutorial. Clinical Biomechanics; 19(4):415-428. 2)C.J.C. Lamoth, A. Daffertshofer, O.G. Meijer, G.L. Moseley, P.i.J.M. Wuisman & P. Beek (2004). Effects of experimentally induced pain and fear of pain on trunk coordination and back muscle activity during walking. Clinical Biomechanics; 19(6):551-563. 3)Olney, S.J., Griffin, M.P. & McBride, I.D. (1998) Multivariate examination of data from gait analysis of persons with stroke. Physical Therapy; 78(8): 814-828. 4)Troje, N. F. (2002). Decomposing biological motion: A framework for analysis and synthesis of human gait patterns. Journal of Vision, 2:371-387 Discussion 1) PCA allows for data clustering and extraction of movement features which are commonly present in different performers and conditions. Here we use it primarily for data reduction. 2) Since PCA modes combine different physical features, functional interpretation of identified modes is difficult and the value of this technique in rapidly facilitating insight into movement control strategies remains to be seen. It seems that one has to accept that complicated movements require more complicated analyses and that classification/quantification in terms of single scalar values is invalid. 3) The combination of eigenvector coefficients and its time series helps to identify synergies. Summary A.225 time series were reduced to 9 components accounting for 85-90% of the total variance (log-eigenvalue spectra in Figs. 3&4 upper left panels). B.Subdividing the principal axes (lengths of the eigenvectors in Figs. 3&4 upper right panels) indicate the first mode is common only to legs-only and full-body performance conditions. This mode represents whole body translations/rotation in space. C.Time series (Fig. 3&4 lower left panels) of component 1 are very similar between dancers. Indicating that movement execution is achieved using similar synergies between performers. D.Eigenvector coefficients for component 1 (Fig. 3&4 lower right panels) show that discrimination between conditions can be seen in the forearm and toe markers in the x and z directions. Acknowledgements Figure 1: Whole body marker Placement (32 markers) Figure 2: Log-Eigenvalue Spectra A) full marker set, B) 5 marker subset Results A B Figures 3&4: PCA of kinematic data for 15 performances (5 repetitions in each of 3 conditions) of the movement phrase by S1 (Fig 3) & S2 (Fig 4). A B D AB D Figure 4: S2 Figure 3: S1 C C