Q1Please confirm that given names and surnames have been identified correctly and are presented in the desired order. Q2The reference given here is cited in the text but is missing from the reference list -please make the list complete or remove the reference from the text: "Seay et al., (2011)", "Chang et al., 2008", "Seay et al., 2006", "Callaghan et al., 1999". Q3Please check the hierarchy of the section headings and correct if necessary. Thank you for your assistance. b s t r a c tThe complexity of human gait patterns has become a topic of major interest in motor control and biomechanics. Range of motion is still the preferred method to quantify movement impairment, however, within these traditional linear measures, the inter-segmental coordination and movement variability is normally ignored. A dynamical systems approach using vector coding and circular statistics provides non-linear techniques to quantify coordination and variability. This study provides comprehensive vector coding and circular statistics calculations. Additionally, pelvis-lumbar coordination and coordination variability data obtained from ten healthy young male participants during five walking trials using an optoelectronic system is provided. This novel data can form the baseline information for future studies in this area of research. Finally, a new illustration to present coordination and coordination variability information of gait kinematics, combining the output from the modified vector coding technique with traditional time-series segmental angle data is presented. This technique, when applied to single patients can be beneficial to assess the effect of an intervention on the patient-specific intersegmental coordination pattern with implications to clinical setting.

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A modified vector coding (VC) technique was used to quantify lumbar-pelvic coordination during gait. The outcome measure from the modified VC technique is known as the coupling angle (CA) which can be classified into one of four coordination patterns. This study introduces a new classification for this coordination pattern that expands on a current data analysis technique by introducing the terms in-phase with proximal dominancy, in-phase with distal dominancy, anti-phase with proximal dominancy and anti-phase with distal dominancy. This proposed coordination pattern classification can offer an interpretation of the CA that provides either in-phase or anti-phase coordination information, along with an understanding of the direction of segmental rotations and the segment that is the dominant mover at each point in time. Classifying the CA against the new defined coordination patterns and presenting this information in a traditional time-series format in this study has offered an insight into segmental range of motion. A new illustration is also presented which details the distribution of the CA within each of the coordination patterns and allows for the quantification of segmental dominancy. The proposed illustration technique can have important implications in demonstrating gait coordination data in an easily comprehensible fashion by clinicians and scientists alike.

The purpose of this study was to determine accurately the magnitude and changes of intra-cycle velocity fluctuation (Vfluc), maximum (Vmax) and minimum velocity (Vmin) of the center of mass during a maximum 200 m frontcrawl swim, and to examine whether they are associated with performance. Performance was indicated by the mean velocity (Vmean) of the stroke cycle (SC) in the swimming direction. The relative Vfluc, Vmax and Vmin were also calculated as a percentage of Vmean, while Vfluc was calculated for all three directions. Eleven male swimmers of national/international level participated in this study and their performance was recorded with four below- and two above-water-synchronized cameras. Four SCs were analyzed for the 200 m swim (one for each 50 m). Anthropometric data were calculated by the elliptical zone method. Vmean generally decreased throughout the test. Vmax and Vmin were positively correlated to performance and were significantly higher in SC1 than in the other SCs. However, the relative Vmax and Vmin values were remarkably consistent during the 200 m and not associated with performance. Despite the noteworthy magnitude of Vfluc in all directions, they were in general not correlated with performance and there were no significant changes during the test.

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This study aims to develop a numerical method that can be used to investigate the cushioning properties of different insole materials on a subject-specific basis. Diabetic footwear and orthotic insoles play an important role for the reduction of plantar pressure in people with diabetes (type-2). Despite that, little information exists about their optimum cushioning properties. A new in-vivo measurement based computational procedure was developed which entails the generation of 2D subject-specific finite element models of the heel pad based on ultrasound indentation. These models are used to inverse engineer the material properties of the heel pad and simulate the contact between plantar soft tissue and a flat insole. After its validation this modelling procedure was utilised to investigate the importance of plantar soft tissue stiffness, thickness and loading for the correct selection of insole material. The results indicated that heel pad stiffness and thickness influence plantar pressure but not the optimum insole properties. On the other hand loading appears to significantly influence the optimum insole material properties. These results indicate that parameters that affect the loading of the plantar soft tissues such as body mass or a person's level of physical activity should be carefully considered during insole material selection.

The optimisation of undulatory underwater swimming is highly important in competitive swimming performance. Nineteen kinematic variables were identified from previous research undertaken to assess undulatory underwater swimming performance. The purpose of the present study was to determine which kinematic variables were key to the production of maximal undulatory underwater swimming velocity. Kinematic data at maximal undulatory underwater swimming velocity were collected from 17 skilled swimmers. A series of separate backward-elimination analysis of covariance models was produced with cycle frequency and cycle length as dependent variables (DVs) and participant as a fixed factor, as including cycle frequency and cycle length would explain 100% of the maximal swimming velocity variance. The covariates identified in the cycle-frequency and cycle-length models were used to form the saturated model for maximal swimming velocity. The final parsimonious model identified three covariates (maximal knee joint angular velocity, maximal ankle angular velocity and knee range of movement) as determinants of the variance in maximal swimming velocity (adjusted-r2 = 0.929). However, when participant was removed as a fixed factor there was a large reduction in explained variance (adjusted r2 = 0.397) and only maximal knee joint angular velocity continued to contribute significantly, highlighting its importance to the production of maximal swimming velocity. The reduction in explained variance suggests an emphasis on inter-individual differences in undulatory underwater swimming technique and/or anthropometry. Future research should examine the efficacy of other anthropometric, kinematic and coordination variables to better understand the production of maximal swimming velocity and consider the importance of individual undulatory underwater swimming techniques when interpreting the data.