PO-0015 THE EFFECT OF BAREFOOT COBBLESTONE WALKING ON PATTERNS OF LIMB MOVEMENT Sport and Exercise Science Research Centre School of Applied Sciences Bruno G. Straiotto, Darren C. James and John P. Seeley Introduction Results Understanding how individuals adapt their gait when walking over different surfaces can have important implications in relation to locomotion generally and for individuals whose movement is compromised by disease or ageing. The objective of this study was to examine body segment coordination of human gait for movement over two different surfaces. This was achieved using standard kinematic analysis techniques and through application of principal component analysis (PCA) to the data obtained. PCA decomposition of the main movement patterns of gait allowed identification of significant differences in PMC1 (p=0.02), PMC3 (p=0.04) and PMC4 (p=0.01) between FS and CS. Eigenvalues of the first four principal movement components of walking on flat surface (FS) and cobblestones (CS). The eigenvector coefficients provided information about the contribution of different body segments to particular movement components. For both conditions, several of the PMCs were related to forward movement, and for CS bodily adjustments in relation to posture and balance were recorded. Principal Movement PMC FS Mean (SD) PMC CS Mean (SD) Paired t-Test PM1 46% (±2%) 52% (±4%) 0.02 PM2 22% (±4%) 21% (±5%) 0.82 PM3 14% (±2%) 12% (±2%) 0.04 PM4 9% (±2%) 7% (±2%) 0.01 Methods Eight subjects (2 females, 6 males; age 33±5 years; mass 76±14 kg; height 1.7±0.1 m (mean±SD)) performed barefoot walking trials across a flat surface (FS) and a destabilizing cobblestone surface (CS). Movements in each condition were recorded in three dimensions from a whole body marker set. Marker positions were recorded using an eight-camera, motion-capture system at 60 Hz. The walking data were normalized to a constant gait cycle using a standard method (“normalized to 101%”). The marker coordinates were interpreted as 105-dimensional posture vectors. These vectors were centred and re-scaled to unit variance for each trial and submitted to PCA to identify the principal movement components (PMCs) for each condition. The first four principal components covered approximately 90% (FS) and 92% (CS) of data variability. Flat surface Cobblestone Surface Conclusion There were differences in the PMCs associated with the subjects’ strategies to control their stability when the system was affected by external factors - in this case the uneven nature of the cobblestone surface. Whilst most eigenvalues were significantly different between conditions, the comparative amplitudes reported were not expected given the distinct difference between level walking and that over a destabilising cobblestone surface. The approach of PCA has theoretical benefit as the PMCs describe the behaviour of the whole system; however, the subtle intricacies of movement appear to be best described by the eigenvector coefficients, as demonstrated here. Bibliography Daffertshofer, et al. 2004. Clin. Biomech. 19, 415–428. Federolf et al., 2012. J. Biomech. 45, 1127–1132. Troje, 2002. J. Vision 2, 371–387. Contact information: Bruno Straiotto: straiotb@lsbu.ac.uk