Semi-Supervised First-Person Activity Recognition in Body-Worn Video

Body-worn cameras are now commonly used for logging daily life, sports, and law enforcement activities, creating a large volume of archived footage. This paper studies the problem of classifying frames of footage according to the activity of the camera-wearer with an emphasis on application to real-world police body-worn video. Real-world datasets pose a different set of challenges from existing egocentric vision datasets: the amount of footage of different activities is unbalanced, the data contains personally identifiable information, and in practice it is difficult to provide substantial training footage for a supervised approach. We address these challenges by extracting features based exclusively on motion information then segmenting the video footage using a semi-supervised classification algorithm. On publicly available datasets, our method achieves results comparable to, if not better than, supervised and/or deep learning methods using a fraction of the training data. It also shows promising results on real-world police body-worn video

Estimating the outcome of spreading processes on networks with incomplete information: a mesoscale approach

Recent advances in data collection have facilitated the access to time-resolved human proximity data that can conveniently be represented as temporal networks of contacts between individuals. While this type of data is fundamental to investigate how information or diseases propagate in a population, it often suffers from incompleteness, which possibly leads to biased conclusions. A major challenge is thus to estimate the outcome of spreading processes occurring on temporal networks built from partial information. To cope with this problem, we devise an approach based on Non-negative Tensor Factorization (NTF) -- a dimensionality reduction technique from multi-linear algebra. The key idea is to learn a low-dimensional representation of the temporal network built from partial information, to adapt it to take into account temporal and structural heterogeneity properties known to be crucial for spreading processes occurring on networks, and to construct in this way a surrogate network similar to the complete original network. To test our method, we consider several human-proximity networks, on which we simulate a loss of data. Using our approach on the resulting partial networks, we build a surrogate version of the complete network for each. We then compare the outcome of a spreading process on the complete networks (non altered by a loss of data) and on the surrogate networks. We observe that the epidemic sizes obtained using the surrogate networks are in good agreement with those measured on the complete networks. Finally, we propose an extension of our framework when additional data sources are available to cope with the missing data problem

Muscle synergy analysis of lower-limb movements

Dissertação de mestrado integrado em Biomedical Engineering (área de especialização em Medical Electronics)Neurological disorders and trauma often lead to impaired lower-limb motor coordination. Understanding how muscles combine to produce movement can directly benefit assistive solutions to those afflicted with these impairments. A theory in neuromusculoskeletal research, known as muscle synergies, has shown promising results in applications for this field. This hypothesis postulates that the Central Nervous System controls motor tasks through the time-variant combinations of modules (or synergies), each representing the co-activation of a group of muscles. There is, however, no unifying, evidence-based framework to ascertain muscle synergies, as synergy extraction methods vary greatly in the literature. Publications also focus on gait analysis, leaving a knowledge gap when concerning motor tasks important to daily life such as sitting and standing. The purpose of this dissertation is the development of a robust, evidence-based, task-generic synergy extraction framework unifying the divergent methodologies of this field of study, and to use this framework to study healthy muscle synergies on several activities of daily living: walking, sit-to-stand, stand-to-sit and knee flexion and extension. This was achieved by designing and implementing a cross-validated Non-Negative Matrix Factorization process and applying it to muscle electrical activity data. A preliminary study was undertaken to tune this configuration regarding cross-validating proportions, data structuring prior to factorization and evaluating criteria quantifying accuracy in modularity findings. Muscle synergies results were then investigated for different performing speeds to determine if their structure differed, and for consistency across subjects, to ascertain if a common set of muscle synergies underlay control on all subjects equally. Results revealed that the implemented framework was consistent in its ability to capture modularity (p < 0:05). The movements’ synergies also did not differ across the studied range of speeds (except one module in Knee Flexion) (p < 0:05). Additionally, a common set of muscle synergies was present across several subjects (p < 0:05), but shared commonality across every participant was only observed for the walking trials, for which much larger amounts of data were collected. Overall, the established framework is versatile and applicable for different lower-limb movements; muscle synergies findings for the examined movements may also be used as control references in assistive devices.As perturbações e traumas neurológicos afetam frequentemente a coordenação motora dos membros inferiores. Uma teoria recente em investigação neuromusculo-esquelética, denominada de sinergias musculares, tem demonstrado resultados promissores em soluções de assistência à população afetada por estes distúrbios. Esta teoria propõe que o Sistema Nervoso Central controla as tarefas motoras através de combinações variantes no tempo de módulos (ou sinergias), sendo que cada um representa a co-ativação de um grupo de músculos. No entanto, não existe nenhum processo uniformizante, empiricamente justificado para determinar sinergias musculares, porque os métodos de extração de sinergias variam muito na literatura. Para além disso, as publicações normalmente focam-se em análise da marcha, deixando uma lacuna de conhecimento em tarefas motoras do dia-a-dia, tais como sentar e levantar. O objetivo desta dissertação é o desenvolvimento de um processo robusto, genérico e empiricamente justificado de extração de sinergias em várias tarefas motoras, unindo as metodologias divergentes neste campo de estudo, e subsequentemente utilizar este processo para estudar sinergias musculares de sujeitos saudáveis em várias atividades do dia-a-dia: marcha, erguer-se de pé partir de uma posição sentada, sentar-se a partir de uma posição de pé e extensão e flexão do joelho. Isto foi alcançado através da implementação de um processo de cross-validated Non-Negative Matrix Factorization e subsequente aplicação em dados de atividade elétrica muscular. Um estudo preliminar foi realizado para configurar este processo relativamente às proporções de cross-validation, estruturação de dados antes da fatorização e seleção de critério que quantifique o sucesso da representação modular dos dados. Os resultados da extração de sinergias de diferentes velocidades de execução foram depois examinados no sentido de descobrir se este fator influenciava a estrutura dos módulos motores, assim como se semelhanças entre as sinergias de diferentes sujeitos apontavam para um conjunto comum de sinergias musculares subjacente ao controlo do movimento. Os resultados revelaram que o processo implementado foi consistente na sua capacidade de capturar a modularidade nos dados recolhidos (p < 0:05). As sinergias de todos os movimentos também não diferiram para toda a gama de velocidades estudada (exceto um módulo na flexão do joelho) (p < 0:05). Por fim, um conjunto comum de sinergias musculares esteve presente em vários sujeitos (p < 0:05), mas só esteve presente em todos os sujeitos de igual forma para a marcha, para a qual a quantidade de dados recolhida foi muito maior. Globalmente, o processo implementado é versátil e aplicável a diferentes movimentos dos membros inferiores; os resultados das sinergias musculares para os movimentos examinados podem também ser utilizado como referências de controlo para dispositivos de assistência