Variáveis Psicológicas e Desempenho Acadêmico: Uma Análise Da Existência de Correlação Canônica

A análise multivariada é um conjunto de técnicas estatísticas que permite a análise e interpretação de conjuntos de dados de natureza quantitativa com grande número de variáveis de forma simplificada. Dentre o rol de técnicas encontra-se a análise de correlação canônica (ACC) que visa estabelecer estrutura de relação entre dois grupos de variáveis por meio de combinações lineares que maximizem a correlação entre ambos. O objetivo deste artigo foi utilizar a técnica de ACC para analisar a correlação entre três variáveis psicológicas e quatro variáveis de desempenho acadêmic

View and clothing invariant gait recognition via 3D human semantic folding

A novel 3-dimensional (3D) human semantic folding is introduced to provide a robust and efficient gait recognition method which is invariant to camera view and clothing style. The proposed gait recognition method comprises three modules: (1) 3D body pose, shape and viewing data estimation network (3D-BPSVeNet); (2) gait semantic parameter folding model; and (3) gait semantic feature refining network. First, 3D-BPSVeNet is constructed based on a convolution gated recurrent unit (ConvGRU) to extract 2-dimensional (2D) to 3D body pose and shape semantic descriptors (2D-3D-BPSDs) from a sequence of gait parsed RGB images. A 3D gait model with virtual dressing is then constructed by morphing the template of 3D body model using the estimated 2D-3D-BPSDs and the recognized clothing styles. The more accurate 2D-3D-BPSDs without clothes are then obtained by using the silhouette similarity function when updating the 3D body model to fit the 2D gait. Second, the intrinsic 2D-3D-BPSDs without interference from clothes are encoded by sparse distributed representation (SDR) to gain the binary gait semantic image (SD-BGSI) in a topographical semantic space. By averaging the SD-BGSIs in a gait cycle, a gait semantic folding image (GSFI) is obtained to give a high-level representation of gait. Third, a gait semantic feature refining network is trained to refine the semantic feature extracted directly from GSFI using three types of prior knowledge, i.e., viewing angles, clothing styles and carrying condition. Experimental analyses on CMU MoBo, CASIA B, KY4D, OU-MVLP and OU-ISIR datasets show a significant performance gain in gait recognition in terms of accuracy and robustness

The utility of gait as a biological characteristic in forensic investigations – An empirical examination of movement pattern variation using biomechanical and anthropological principles

Forensic gait analysis is generally defined as the analysis of gait features from video footage to assist in criminal investigations. Although an attractive means to detect suspects since data can be collected from a distance without their knowledge, forensic gait analysis presently lacks method validation and quality standards, not only due to insufficient research, but also because certain scientific foundations, such as the assumption of gait uniqueness, have not been adequately addressed. To test the scientific basis of this premise, a suitable dataset replicating an ideal forensic gait analysis scenario was compiled from the Karlsruhe Institute of Technology (Germany) database. Biomechanical analysis of sagittal plane human motion in the bilateral shoulder, elbow, hip, knee, and ankle joints was conducted across complete gait cycles of twenty participants, to investigate the degree to which intraindividual variation impacts interindividual variation, according to the following aims: (1) to better understand the relationship between form (anatomy) and function (physiology) of human gait, (2) to investigate the basis of gait uniqueness by examining similarities and differences in joint angles, and (3) to build upon current theoretical foundations of gait-based human identification. The findings indicate different degrees of movement asymmetry given body region and gait sub-phase, thereby challenging previous methods employing interchangeable use of bilateral motion data, and the use of ‘average’ gait cycles to represent the gait of an individual irrespective of body side. Furthermore, interindividual variability in all five joints is influenced by body side to different extents depending on gait sub-phase and body region, thereby challenging the claim of holistic uniqueness of gait features across all body regions and gait events. Given the findings of this thesis and paucity regarding empirical basis to support expertise, exerting caution when evaluating gait-based evidence admissibility is highly recommended, since the utility of gait in identification is currently limited

Artificial Intelligence for Data Analysis and Signal Processing

Artificial intelligence, or AI, currently encompasses a huge variety of fields, from areas such as logical reasoning and perception, to specific tasks such as game playing, language processing, theorem proving, and diagnosing diseases. It is clear that systems with human-level intelligence (or even better) would have a huge impact on our everyday lives and on the future course of evolution, as it is already happening in many ways. In this research AI techniques have been introduced and applied in several clinical and real world scenarios, with particular focus on deep learning methods. A human gait identification system based on the analysis of inertial signals has been developed, leading to misclassification rates smaller than 0.15%. Advanced deep learning architectures have been also investigated to tackle the problem of atrial fibrillation detection from short length and noisy electrocardiographic signals. The results show a clear improvement provided by representation learning over a knowledge-based approach. Another important clinical challenge, both for the patient and on-board automatic alarm systems, is to detect with reasonable advance the patterns leading to risky situations, allowing the patient to take therapeutic decisions on the basis of future instead of current information. This problem has been specifically addressed for the prediction of critical hypo/hyperglycemic episodes from continuous glucose monitoring devices, carrying out a comparative analysis among the most successful methods for glucose event prediction. This dissertation also shows evidence of the benefits of learning algorithms for vehicular traffic anomaly detection, through the use of a statistical Bayesian framework, and for the optimization of video streaming user experience, implementing an intelligent adaptation engine for video streaming clients. The proposed solution explores the promising field of deep learning methods integrated with reinforcement learning schema, showing its benefits against other state of the art approaches. The great knowledge transfer capability of artificial intelligence methods and the benefits of representation learning systems stand out from this research, representing the common thread among all the presented research fields