This study quantified the changes in pedaling mechanics and energy expenditure accompanying a posterior shift in cleat placement during prolonged cycling. Six male competitive cyclists participated. Each subject was asked to complete two separate hour long rides using traditional cleat placement and a novel heel placement, respectively. Expired gasses, kinematics, and EMG from 7 lower limb muscles were collected at three time intervals during each ride. No significant difference in O2 utilization was seen (p=0.905). A significant difference was seen in sagittal plane knee angle (p=0.008) and angular velocity (p=0.003) in the heel condition, demonstrating a more extended knee and lower peaks in angular velocity. Musculo-tendon kinematic data showed no differences. Tibialis anterior (TA) iEMG was higher in the heel condition, and SOL and TA showed differences in timing between conditions. These results demonstrate changes in ankling patterns and knee joint kinematics as adaptations to heel pedaling. / School of Physical Education, Sport, and Exercise Science

Lee Kwok Wai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 144-148). / Text in English; abstracts in English and Chinese. / Lee Kwok Wai. / Abstract --- p.i / 摘要 --- p.ii / Acknowledgements --- p.iii / Table of Content --- p.iv / List of Figures & Tables --- p.viii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Motion Symmetry and its Application in Classification of Articulated Motions --- p.5 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.1.1 --- Motivation & Related Works --- p.7 / Chapter 2.1.2 --- Transformation Matrix for a Rigid Motion --- p.8 / Chapter 2.2 --- Review of Motion Estimation --- p.9 / Chapter 2.2.1 --- Motion Estimation & Motion Fields --- p.9 / Chapter 2.2.2 --- Motion Field Construction from Optical Flow --- p.10 / Chapter 2.2.3 --- Motion Field Construction from Image Matching --- p.12 / Chapter 2.3 --- Motion Symmetry --- p.13 / Chapter 2.3.1 --- Problem Definition --- p.13 / Chapter 2.3.2 --- Definitions of Transformation Symmetry & Anti- symmetry --- p.14 / Chapter 2.3.2.1 --- Translation Symmetry --- p.15 / Chapter 2.3.2.2 --- Translation Anti-symmetry --- p.16 / Chapter 2.3.2.3 --- Rotation Symmetry --- p.17 / Chapter 2.3.2.4 --- Rotation Anti-symmetry --- p.18 / Chapter 2.3.2.5 --- Scaling Symmetry … --- p.18 / Chapter 2.3.2.6 --- Scaling Anti-symmetry --- p.19 / Chapter 2.3.3 --- Transformation Quasi-symmetry & Quasi-anti- symmetry --- p.19 / Chapter 2.3.4 --- Symmetric Transform of a Transformation --- p.19 / Chapter 2.3.5 --- Symmetric Motions & Periodic Symmetric Motions --- p.20 / Chapter 2.3.6 --- Transformation Vector Fields of Symmetric Motions --- p.20 / Chapter 2.4 --- Detection of Motion Symmetry --- p.23 / Chapter 2.4.1 --- Model-based Motion Parameter Analysis --- p.24 / Chapter 2.4.2 --- Transformation Matrices Analysis --- p.25 / Chapter 2.4.3 --- Simultaneous Resultant Transformation Matrix Analysis --- p.31 / Chapter 2.4.4 --- Motion Symmetry as a Continuous Feature --- p.38 / Chapter 2.5 --- Illustrations & Results … --- p.39 / Chapter 2.5.1 --- Experiment 1: Randomly Generated Data --- p.39 / Chapter 2.5.2 --- Experiment 2: Symmetry Axis for a 3D object --- p.41 / Chapter 2.6 --- Summary & Discussion --- p.44 / Chapter 2.7 --- Appendices --- p.47 / Chapter 2.7.1 --- Appendix 1: Reflection of a Point about a Line --- p.47 / Chapter 2.7.2 --- Appendix 2: Symmetric Transform of a Transformation --- p.49 / Chapter Chapter 3 --- Motion Representation by Feedforward Neural Networks --- p.53 / Chapter 3.1 --- Introduction --- p.54 / Chapter 3.2 --- Motion Modeling in Animation --- p.57 / Chapter 3.2.1 --- Parameterized Motion Representation --- p.58 / Chapter 3.2.2 --- Problems of Motion Analysis --- p.62 / Chapter 3.3 --- Multi-value Regression by Feedforward Neural Networks --- p.66 / Chapter 3.3.1 --- Review of Multi-value Regression Methods --- p.66 / Chapter 3.3.2 --- Problem Definition --- p.68 / Chapter 3.3.3 --- Proposed Methods --- p.69 / Chapter 3.3.3.1 --- Modular Networks with Verification Module --- p.69 / Chapter (a) --- Validation by Decoding --- p.70 / Chapter (b) --- Validation by inverse mapping --- p.71 / Chapter 3.3.3.2 --- Partition Algorithm --- p.73 / Chapter 3.4 --- Illustration & Results --- p.76 / Chapter 3.4.1 --- Cylindrical Spiral Function --- p.76 / Chapter 3.4.2 --- Elongated Cylindrical Spiral Function --- p.79 / Chapter 3.4.3 --- Cylindrical Spiral Surface --- p.83 / Chapter 3.4.4 --- S-curve Data --- p.87 / Chapter 3.4.5 --- Inverse Sine function --- p.89 / Chapter 3.5 --- Motion Analysis … --- p.91 / Chapter 3.6 --- Summary & Discussion --- p.94 / Chapter Chapter 4 --- Motion Representation by Recurrent Neural Networks --- p.98 / Chapter 4.1 --- Introduction --- p.99 / Chapter 4.1.1 --- Recurrent Neural Networks (RNNs) --- p.99 / Chapter 4.1.2 --- Fully & Partially Recurrent Neural Networks --- p.101 / Chapter 4.1.3 --- Back-propagation Training Algorithm --- p.105 / Chapter 4.2 --- Sequence Encoding by Recurrent Neural Networks --- p.106 / Chapter 4.2.1 --- Random Binary Sequence --- p.107 / Chapter 4.2.2 --- Angular Positions of Clock Needles --- p.108 / Chapter 4.2.3 --- Absolute Positions of Clock Needles´ةTips --- p.109 / Chapter 4.2.4 --- Henon Time Series --- p.111 / Chapter 4.2.5 --- Ikeda Time Series … --- p.112 / Chapter 4.2.6 --- Single-Input-Single-Output (SISO) Non-linear System --- p.114 / Chapter 4.2.7 --- Circular Trajectory --- p.115 / Chapter 4.2.8 --- Number Trajectories --- p.118 / Chapter 4.3 --- Animation Generation by Recurrent Neural Networks --- p.123 / Chapter 4.3.1 --- Storage & Generation of Animations --- p.127 / Chapter 4.3.2 --- Interpolation between two Motion Segments --- p.127 / Chapter 4.4 --- Motion Analysis by Recurrent Neural Networks --- p.129 / Chapter 4.5 --- Experimental Results --- p.130 / Chapter 4.5.1 --- Network Training --- p.130 / Chapter 4.5.2 --- Motion Interpolation --- p.134 / Chapter 4.5.3 --- Motion Recognition --- p.135 / Chapter 4.6 --- Summary & Discussion --- p.138 / Chapter Chapter 5 --- Conclusion --- p.141 / References --- p.144