Gait Recognition from Motion Capture Data

Gait recognition from motion capture data, as a pattern classification discipline, can be improved by the use of machine learning. This paper contributes to the state-of-the-art with a statistical approach for extracting robust gait features directly from raw data by a modification of Linear Discriminant Analysis with Maximum Margin Criterion. Experiments on the CMU MoCap database show that the suggested method outperforms thirteen relevant methods based on geometric features and a method to learn the features by a combination of Principal Component Analysis and Linear Discriminant Analysis. The methods are evaluated in terms of the distribution of biometric templates in respective feature spaces expressed in a number of class separability coefficients and classification metrics. Results also indicate a high portability of learned features, that means, we can learn what aspects of walk people generally differ in and extract those as general gait features. Recognizing people without needing group-specific features is convenient as particular people might not always provide annotated learning data. As a contribution to reproducible research, our evaluation framework and database have been made publicly available. This research makes motion capture technology directly applicable for human recognition.Comment: Preprint. Full paper accepted at the ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans. 18 pages. arXiv admin note: substantial text overlap with arXiv:1701.00995, arXiv:1609.04392, arXiv:1609.0693

Automated Markerless Extraction of Walking People Using Deformable Contour Models

We develop a new automated markerless motion capture system for the analysis of walking people. We employ global evidence gathering techniques guided by biomechanical analysis to robustly extract articulated motion. This forms a basis for new deformable contour models, using local image cues to capture shape and motion at a more detailed level. We extend the greedy snake formulation to include temporal constraints and occlusion modelling, increasing the capability of this technique when dealing with cluttered and self-occluding extraction targets. This approach is evaluated on a large database of indoor and outdoor video data, demonstrating fast and autonomous motion capture for walking people

Orthopedic management of the extremities in patients with Morquio A syndrome.

BackgroundMusculoskeletal involvement in Morquio A syndrome (mucopolysaccharidosis IVA; MPS IVA) contributes significantly to morbidity and mortality. While the spinal manifestations of the disorder have received considerable attention in the literature, there have been few reported studies to date to guide the management of the orthopedic problems associated with the lower and upper extremities.PurposeThe objective was to develop recommendations for the management of the extremities in patients with Morquio A syndrome.MethodsA group of specialists in orthopedics, pediatrics and genetics with experience in the management of Morquio A patients convened to review and discuss current clinical practices and to develop preliminary recommendations. Evidence from the literature was retrieved. Recommendations were further refined until consensus was reached.Results and conclusionsThis present article provides a detailed review and discussion of the lower and upper extremity deformities in Morquio A syndrome and presents recommendations for the assessment and treatment of these complications. Key issues, including the importance of early diagnosis and the implications of medical therapy, are also addressed. The recommendations herein represent an attempt to develop a uniform and practical approach to managing patients with Morquio A syndrome and improving their outcomes