Data’s Hidden Data: Qualitative Revelations of Sports Efficiency Analysis brought by Neural Network Performance Metrics

In the study of effectiveness and efficiency of an athlete’s performance, intelligent systems can be applied on qualitative approaches and their performance metrics provide useful information on not just the quality of the data, but also reveal issues about the observational criteria and data collection context itself. 2000 executions of two similar exercises, with different levels of complexity, were collected through a single inertial sensor applied on the fencer’s weapon hand. After the signals were split into their key segments through Dynamic Time Warping, the extracted features and respective qualitative evaluations were fed into a Neural Network to learn the patterns that distinguish a good from a bad execution. The performance analysis of the resulting models returned a prediction accuracy of 76.6% and 72.7% for each exercise, but other metrics pointed to the data suffering from high bias. This points towards an imbalance in the qualitative criteria representation of the bad executions, which can be explained by: i) reduced number of samples; ii) ambiguity in the definition of the observation criteria; iii) a single sensor being unable to fully capture the context without taking the actions of the other key body segments into account

Stability of patterns of behavior in the butterfly technique of the elite swimmers

The purpose of this study was to find patterns in the butterfly swimming technique, with an adaptation of the Behavioral Observation System Tech. This, as an instrument for ad-hoc qualitative analysis, enables the study of the stability of the technical implementation. When used in the training of swimmers, analysis can reduce the variability of behavioral tuning swimming technique. Through the analysis of temporal patterns (T-pattern) and a sequence of five cycles running at hand maximum speed, the behavior of four technical Portuguese elite swimmers, with a record of 259 alphanumeric codes and a total of 160 configurations, were studied. The structure of the original instrument, based on a mixed system of categories and formats Field, can record technical features, observed during the execution of hand cycles. The validity was ensured through the index of intra-observer reliability (95%) and inter-observer accuracy (96%). To detect patterns in each swimmer, the Theme 5.0 software was used, which allowed to identify the stable structures of technical performance within a critical interval of time (p <0.05) - t-patterns. The patterns were different, adjusting to the characteristics of technical implementation of the swimmers. It was found that the swimmer can create settings with different levels of structure complexity, depending on the implementation of changes within the hand cycle. Variations of codes in each configuration obtained using the SOCTM, allowed determining the differences between swimmers. However, the records showed a clear behavioral similarity when comparing the result with a general pattern of the butterfly technique. The potential quality of this instrument seems to be important due to the patterns obtained from a temporal sequence. Key pointsThe patterns were different, adjusting to the characteristics of technical implementation of the swimmers.The swimmer can make settings with different levels of structure complexity, depending on the implementation of changes within the hand cycle.Variations of codes in each configuration obtained using the SOCTM, allowed determining the differences between swimmers.The records showed a clear behavioral similarity when comparing the result with a general pattern of the butterfly technique.The potential quality of this instrument seems to be important due to the patterns obtained from a temporal sequence.info:eu-repo/semantics/publishedVersio

Programação Genética em Aplicações Gráficas para Jogos: Simulação e Visualização de Plantas utilizando Flash Actionscript

Dissertação de Mestrado em InformáticaTirando vantagem dos poderosos mecanismos existentes na natureza, o objectivo deste trabalho foi o de criar uma aplicação capaz de evoluir estruturas de plantas em Flash. Isto foi possível através da combinação da gramática L-System, que define a arquitectura da planta, e da Programação Genética, que evolui a planta produzida e gera uma população de filhos que diferem bastante dos pais originais em apenas algumas gerações. O que este programa faz é a Validação da Sintaxe, a Produção e a interpretação da planta L-System, pegando no axioma e regras de produção dadas e fazendo um constante substituição dos símbolos, pelos seus respectivos sucessores durante várias iterações. De seguida, uma palavra é lida e cada comando interpretado para fazer o seu desenho. Quando as diferentes plantas são atribuídas com um valor de aptidão, pela sua aparência estética, as palavras que compõem a sua estrutura, são enviadas para a Programação Genética a fim de servirem de, indivíduos. Aí os indivíduos são seleccionados e os seus ramos aleatoriamente trocados entre pares de plantas de forma a gerar um par,de plantas filho, sendo de, novo enviadas para a Interpretação do L System,de forma a serem desenhadas. Uma vez que as novas gerações de plantas são visualmente distintas, das estruturas dos pais, conseguimos evoluir plantas L-Systems através, da Programação Genética.Taking advantage of the powerful mechanisms existing in nature, the purpose of this work was to create an application capable of evolving a plant structure in Flash. It does so by combining the LSystem grammar, which defines the architecture of the plant, and Genetic Programming, which will evolve the produced L-Systems and generate a population of children quite different from their original parents in just a few generations. What this program does is the Syntax Validation, the Production and the Interpretation of the L-System plant, taking the given axiom and production rules and doing a constant replacement of the symbols with their respective successors during several iterations. Then the word is read and each command interpreted to draw the plant. When the different plants are given a fitness value for their A esthetic appearance, the words that define their structures are sent to the Genetic Programming to serve as individuals. There the individuals are selected and their branches randomly switched between parent plants in order to create a pair of child plants, being those sent again to the L-System's Interpretation step to be drawn. Since the new generations of plants are visually distinct from their parents’ structures, we can evolve L-System plants through Genetic Programming

Hybrid Quality Inspection for the Automotive Industry: Replacing the Paper-Based Conformity List through Semi-Supervised Object Detection and Simulated Data

The still prevalent use of paper conformity lists in the automotive industry has a serious negative impact on the performance of quality control inspectors. We propose instead a hybrid quality inspection system, where we combine automated detection with human feedback, to increase worker performance by reducing mental and physical fatigue, and the adaptability and responsiveness of the assembly line to change. The system integrates the hierarchical automatic detection of the non-conforming vehicle parts and information visualization on a wearable device to present the results to the factory worker and obtain human confirmation. Besides designing a novel 3D vehicle generator to create a digital representation of the non conformity list and to collect automatically annotated training data, we apply and aggregate in a novel way state-of-the-art domain adaptation and pseudo labeling methods to our real application scenario, in order to bridge the gap between the labeled data generated by the vehicle generator and the real unlabeled data collected on the factory floor. This methodology allows us to obtain, without any manual annotation of the real dataset, an example-based F1 score of 0.565 in an unconstrained scenario and 0.601 in a fixed camera setup (improvements of 11 and 14.6 percentage points, respectively, over a baseline trained with purely simulated data). Feedback obtained from factory workers highlighted the usefulness of the proposed solution, and showed that a truly hybrid assembly line, where machine and human work in symbiosis, increases both efficiency and accuracy in automotive quality control