O objetivo desta pesquisa é a captura, detecção e classificação de movimentos humanos anormais (tremores, vibrações, espasmos e contrações musculares) e movimentos normais do cotidiano. Um dispositivo não invasivo, desenvolvido pelos alunos de iniciação científica do CPGEI-UTFPR, baseado no componente integrado eletrônico acelerômetro, foi colocado no pulso de voluntários para a captura dos movimentos objetos de estudo. Todos os experimentos foram realizados no laboratório Biota da UTFPR. Os movimentos andando, correndo, aceno de tchau, batendo palmas e tremores foram capturados de 5 voluntários adultos. Um pré-processamento off-line é efetuado por um programa desenvolvido na linguagem Matlab 6.5, o qual extrai as principais características que devem refletir a amplitude, intensidade e frequência de cada movimento e fornecer um arquivo contendo os padrões supervisionados. Utilizou-se uma rede neural fuzzy do tipo FAN (Free Associative Neuron) e uma rede neural MLP (Multi-Layer Perceptron), para classificar um banco de dados contendo um total de 375 padrões, dos quais 250 padrões (50 de cada movimento) para a fase de treinamento e 125 padrões (25 de cada movimento) para a fase de validação dos dados. Os percentuais de acerto médio obtidos na classificação dos dados capturados de 5 indivíduos foram de 81,6% para a rede neural FAN e 72,6% para a rede MLP. Outro experimento foi realizado para capturar os mesmos movimentos do estudo anterior, provenientes de um único indivíduo. De um total de 2100 padrões, 1500 foram utilizados para treinamento (300 de cada movimento) e 600 padrões (120 de cada movimento) para a validação dos dados. Os percentuais de acerto médio na classificação dos dados foram de 98,2% para a rede neural FAN e 96,7% para a rede neural MLP observando-se uma melhora significativa nos resultados. Um último experimento foi realizado acrescentando ao banco de dados mais alguns movimentos executados por um único indivíduo: penteando, aparafusando, círculos, socos no ar e coçando a perna. Os percentuais de acerto médio obtidos na classificação dos dados foram de 99,3% para a rede neural FAN e 99,1% para a rede neural MLP. Os resultados da classificação dos dados para um total de 10 movimentos e padrões elaborados com 13 características foram obtidos baseando-se em um banco de dados contendo um total de 4200 padrões, dos quais 3000 padrões (300 de cada movimento) para a fase de treinamento e 1200 padrões (120 de cada movimento) para a fase de validação dos dados. Neste experimento houve uma melhora ainda mais significativa na classificação dos dados, tendo em vista o acréscimo de 3 novas características aos padrões de treinamento: valores posturais (offset) extraídos dos sinais referentes aos eixos x, y e z do acelerômetro. / The aim of this research is the capture, detection and classification of abnormal human movements (tremors, vibrations, spasms and muscle contractions) and normal movements of everyday life. A non-invasive device, developed by undergraduate students of UTFPR, based on integrated electronic accelerometer, was placed on the wrist of volunteers to capture the movements. All experiments were performed in the laboratory Biota of CPGEI-UTFPR. The movement of walking, running, waving a goodbye, clapping and shaking, were captured in 5 adult volunteers. A pre-processing was done off-line by a program developed using Matlab 6.5, which extracts key features that should reflect the breadth, intensity and frequency of each movement and provide a file containing the standard supervised. We used a fuzzy neural network-type FAN (Free Associative Neuron) and a neural network MLP (Multi-Layer Perceptron) to classify a database containing a total of 375 patterns, of which 250 patterns (50 of each movement) for the training phase and 125 patterns (25 of each movement) to data validation. The average percentage of correct classification of data obtained from 5 individuals, were captured from 81.6% for the neural network FAN and 72.6% for MLP. Another experiment was conducted to capture the same movements in the previous study from a single individual. From a total of 2100 patterns, 1500 were used for training (300 for each movement) and 600 patterns (120 for each movement) for validation. The average percentage of correct classification of the data were 98.2% for the neural network FAN, 96.7% for MLP neural network, observing a significant improvement in the results. A final experiment was performed adding to the database some more movements performed by a single individual: combing, bolting, circles, punching the air and scratching his leg. The average percentage of correct classification of the data obtained were 99.3% for the neural network FAN and 99.1% for MLP neural network. The results of the classification of data for a total of 10 movements and elaborate patterns with 13 features were obtained based on a database containing a total of 4200 patterns, of which 3000 patterns (300 for each movement) for the training and 1200 patterns (120 for each movement) to data validation. In this experiment there was a further improvement in data classification, considering the addition of three new features to the training patterns, postural values (offset) extracted from the signals related to the axes x, y and z of the accelerometer.

O objetivo desta pesquisa é a captura, detecção e classificação de movimentos humanos anormais (tremores, vibrações, espasmos e contrações musculares) e movimentos normais do cotidiano. Um dispositivo não invasivo, desenvolvido pelos alunos de iniciação científica do CPGEI-UTFPR, baseado no componente integrado eletrônico acelerômetro, foi colocado no pulso de voluntários para a captura dos movimentos objetos de estudo. Todos os experimentos foram realizados no laboratório Biota da UTFPR. Os movimentos andando, correndo, aceno de tchau, batendo palmas e tremores foram capturados de 5 voluntários adultos. Um pré-processamento off-line é efetuado por um programa desenvolvido na linguagem Matlab 6.5, o qual extrai as principais características que devem refletir a amplitude, intensidade e frequência de cada movimento e fornecer um arquivo contendo os padrões supervisionados. Utilizou-se uma rede neural fuzzy do tipo FAN (Free Associative Neuron) e uma rede neural MLP (Multi-Layer Perceptron), para classificar um banco de dados contendo um total de 375 padrões, dos quais 250 padrões (50 de cada movimento) para a fase de treinamento e 125 padrões (25 de cada movimento) para a fase de validação dos dados. Os percentuais de acerto médio obtidos na classificação dos dados capturados de 5 indivíduos foram de 81,6% para a rede neural FAN e 72,6% para a rede MLP. Outro experimento foi realizado para capturar os mesmos movimentos do estudo anterior, provenientes de um único indivíduo. De um total de 2100 padrões, 1500 foram utilizados para treinamento (300 de cada movimento) e 600 padrões (120 de cada movimento) para a validação dos dados. Os percentuais de acerto médio na classificação dos dados foram de 98,2% para a rede neural FAN e 96,7% para a rede neural MLP observando-se uma melhora significativa nos resultados. Um último experimento foi realizado acrescentando ao banco de dados mais alguns movimentos executados por um único indivíduo: penteando, aparafusando, círculos, socos no ar e coçando a perna. Os percentuais de acerto médio obtidos na classificação dos dados foram de 99,3% para a rede neural FAN e 99,1% para a rede neural MLP. Os resultados da classificação dos dados para um total de 10 movimentos e padrões elaborados com 13 características foram obtidos baseando-se em um banco de dados contendo um total de 4200 padrões, dos quais 3000 padrões (300 de cada movimento) para a fase de treinamento e 1200 padrões (120 de cada movimento) para a fase de validação dos dados. Neste experimento houve uma melhora ainda mais significativa na classificação dos dados, tendo em vista o acréscimo de 3 novas características aos padrões de treinamento: valores posturais (offset) extraídos dos sinais referentes aos eixos x, y e z do acelerômetro. / The aim of this research is the capture, detection and classification of abnormal human movements (tremors, vibrations, spasms and muscle contractions) and normal movements of everyday life. A non-invasive device, developed by undergraduate students of UTFPR, based on integrated electronic accelerometer, was placed on the wrist of volunteers to capture the movements. All experiments were performed in the laboratory Biota of CPGEI-UTFPR. The movement of walking, running, waving a goodbye, clapping and shaking, were captured in 5 adult volunteers. A pre-processing was done off-line by a program developed using Matlab 6.5, which extracts key features that should reflect the breadth, intensity and frequency of each movement and provide a file containing the standard supervised. We used a fuzzy neural network-type FAN (Free Associative Neuron) and a neural network MLP (Multi-Layer Perceptron) to classify a database containing a total of 375 patterns, of which 250 patterns (50 of each movement) for the training phase and 125 patterns (25 of each movement) to data validation. The average percentage of correct classification of data obtained from 5 individuals, were captured from 81.6% for the neural network FAN and 72.6% for MLP. Another experiment was conducted to capture the same movements in the previous study from a single individual. From a total of 2100 patterns, 1500 were used for training (300 for each movement) and 600 patterns (120 for each movement) for validation. The average percentage of correct classification of the data were 98.2% for the neural network FAN, 96.7% for MLP neural network, observing a significant improvement in the results. A final experiment was performed adding to the database some more movements performed by a single individual: combing, bolting, circles, punching the air and scratching his leg. The average percentage of correct classification of the data obtained were 99.3% for the neural network FAN and 99.1% for MLP neural network. The results of the classification of data for a total of 10 movements and elaborate patterns with 13 features were obtained based on a database containing a total of 4200 patterns, of which 3000 patterns (300 for each movement) for the training and 1200 patterns (120 for each movement) to data validation. In this experiment there was a further improvement in data classification, considering the addition of three new features to the training patterns, postural values (offset) extracted from the signals related to the axes x, y and z of the accelerometer.

The transition from cycling to running has been identified as one of the key determinants of success in triathlon, as it has been suggested that the cycle may affect subsequent running efficiency such that running performance is significantly altered or reduced. It is also suggested that athletes more adapted to the transition itself, rather than purely running or cycling, may be more efficient during the post-cycle running bout. The current study sought to investigate the effects of prior cycling on subsequent selected biomechanical, physiological and perceptual responses of three different athlete groups. Subjects were selected on the basis of their sporting background, and were divided into three groups – triathletes, cyclists and runners. Experimentation required subjects to perform a seven minute treadmill running protocol at 15km.h⁻¹, during which biomechanical (EMG, Stride rate, Stride length, Vertical acceleration), physiological (HR, VO₂, EE) and perceptual (RPE) responses were recorded. After resting, subjects were required to perform a twenty minute stationary cycle at 70% of maximal aerobic power (previously determined), immediately followed by a second seven minute treadmill running protocol during which the same data were collected and compared to those collected during the first run. Biomechanical responses indicate that the cycle protocol had no effect on the muscle activity or vertical acceleration responses of any of the three subject groups, while the triathlete group significantly altered their gait responses in order to preserve running economy. The triathlete group was the least affected when considering the physiological responses, as running economy was preserved for this group. The runner and cyclist groups were significantly affected by the transition, as running economy decreased significantly for these groups. Perceptual responses indicate that athletes more experienced with the transition may find the transition from cycling to running to be easier than those inexperienced in this transition. It is apparent that a high intensity cycle protocol has limited statistical impact on selected biomechanical responses, while physiological and perceptual responses were altered, during a subsequent run, regardless of athlete type. That said, the ability of transition-trained athletes to transition comfortably between disciplines was highlighted, which may have important performance implications.

This thesis describes the modeling of Martial Arts as a markup language. Up until now Martial Arts has already been documented in books, videos, tradition and other methods. Though to represent Martial Arts knowledge consistently and uniformly in a digital era, we introduce the Martial Arts Markup Language (MAML), which is based on XML. Because XML provides a standardized, serializable and portable format, MAML also enables sharing among students, teachers and their peers across different platforms, media and networks. MAML provides the ability, with appropriate XML tools, to document a Martial Arts style in a structured way. To achieve this, we first analyze the aspects that comprise Martial Arts; and how its states and processes relate to one another. We model in MAML describing the stances, transitions, punches, blocks, techniques, combinations, reactions and patterns used in Martial Arts. We discuss the implementation of MAML by observing and extracting the definable aspects in existing Martial Art Instructive Documents. The MAML Schema assures that the details of a Martial Arts Style’s elements are consistent. Current simulation efforts will be explained as well as areas for future development. We have described Martial Arts by observing what has already been done and creating a structured standard to document them. We hope to enable practitioners’ abilities to learn from and develop their arts by providing a resource in which they can interact with. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

[Truncated abstract] Knee osteoarthritis (OA) patients have high levels of pain, functional and strength deficits of the quadriceps, decreased proprioceptive acuity, and increased co-contraction and knee joint loading in gait, compared to age matched controls. The increased knee joint loading in this population occurs most commonly in the medial tibio-femoral compartment, due to increased external adduction moments, and with increasing disease severity there is a concomitant increase in the knee adduction moments. A key finding within the knee OA literature is that dynamic loading in gait, due to increased external adduction moments, strongly predicts pain and radiographic disease progression. Current research has shown that exercise interventions reduce pain and time to complete functional activities; however, the effect of these interventions on knee joint loading and muscular activation in gait is still unclear. In addition, the need for specific knee joint strengthening to cause these alterations has not been investigated and it remains unknown if improvements occur due to specific muscle strengthening or due to some general effect of exercise. Therefore, the primary aim of this research study was to examine the effects of general (upper body) and specific (lower body) resistance training interventions on self-perceived outcomes, neuromuscular function and kinematic, kinetic and muscle activation during gait of OA patients compared with asymptomatic controls. ... The examination of gait data following exercise (Study 4) showed trends for changes in the muscle co-contraction ratios. Specifically, the medial/lateral co-contraction ratio (MLCCR) displayed a trend in early stance where the upper body exercise group increased their lateral muscle activity and the lower body group reduced their lateral muscle activity, and the medial/lateral hamstring co-contraction ratio (HAMCCR) displayed this same trend during loading. The trend toward reduced lateral muscle activation, following lower body resistance training, suggests that specific muscle strengthening may have the ability to alter the load distribution. The kinematic and kinetic variables of gait were unchanged by the exercise interventions, highlighting the sensitivity of muscle activation pattern changes due to muscle strengthening. This thesis provides new insights into the co-contraction strategies utilised by knee OA patients. The directed co-contraction strategy employed by knee OA patients and its relationship to the external adduction moment in gait suggest an attempt to redistribute the loading within the knee joint, most likely in response to pain. Further, we have separated the effects of exercise and found differences in self-perceived outcomes based on exercise specificity. This first examination into muscle co-contraction following resistance training of knee OA patients has highlighted the possibility of alterations to the co-contraction patterns following lower body exercise. However, the implications of altering this muscle activation strategy and the consequent effect on distribution of load within the knee joint requires further consideration.

[Truncated abstract] An important field of research in orthopaedic biomechanics is the elucidation and mathematical modelling of the mechanical response of cartilaginous tissues. Such research has applications in the understanding of joint function and degenerative processes, as well as in surgical planning and simulation, and engineering of tissue replacements. In the case of surgical and tissue engineering applications especially, patient-specific mechanical properties are highly desirable. Unfortunately, obtaining such information would generally involve destructive mechanical testing of patient tissue, thus rendering the tissue functionally unusable. Development of a laser scanning confocal arthroscope (LSCA) within our School will soon allow non-invasive extraction of 3D microstructural images of cartilaginous tissues in vivo. It is also envisaged that, linked to a suitably formulated constitutive formulation, such information could allow estimation of tissue mechanical response without physical biopsy. This thesis describes the development of techniques to potentially allow non-invasive patient-specific estimation of tissue mechanical response based on confocal arthroscopy data. A microstructural constitutive model is developed which is capable of directly incorporating LSCA-derived patient-specific structural information. A fibre composite type homogenisation approach is used as the basis for the model. ... The result is a series of orientation tensors describing the 3D orientation of linear features in the image stack. The developed analysis techniques are used to estimate fibre volume fraction and orientation distribution for each of the meniscal specimens. The developed constitutive model and image-derived structural parameters are finally used to estimate the reaction force history of two meniscal cartilage specimens subjected to partially confined compression. The predictions are made on the basis of the specimens? individual structural condition as assessed by confocal microscopy and involve no tuning of material parameters. Although the model does not reproduce all features of the experimental curves, as an unfitted estimate of mechanical response the prediction is quite accurate. In light of the obtained results it is judged that more general non-invasive estimation of tissue mechanical properties is possible using the developed framework. The likely limitations and potential areas of improvement are discussed.

Thesis (M.App.Sc.) - Carleton University, / Includes bibliographical references (p. 82-88). Also available in electronic format on the Internet.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2009. / Includes bibliographical references.

Thesis (M.S.)--State University of New York at Binghamton, Department of Systems Science and Industrial Engineering, 2009. / Includes bibliographical references (leaves: 150-155).

Manual lifting tasks are the predominant means of transporting materials in industry with many of these tasks being performed in confined spaces. Research has tended to focus on the biomechanical implications of working in small spaces with a decided lack of information about the physiological and perceptual responses in these environments. This holistic study therefore investigated the manner in which the human operator responded to conditions where the ceiling height was lowered and reach demands increased. Thirty-two young physically active male subjects (age: 21.55yr; stature: 1810mm) were recruited to complete a 2-way repeated measures experiment during which four lifting protocols where different combinations of ceiling height (‘normal’ or reduced to 1460mm in height) and reach demands (400mm or 800mm) were tested. A crude postural analysis was conducted while physiological responses were detailed and continuously monitored. Perceptual responses were also assessed. The tasks with a ‘normal’ ceiling height (mean compression forces: 2615N; mean shearing forces: 388N) and the greatest reach distance (mean compression forces: 3655N; mean shearing forces: 386N) placed individuals under the highest strain. Mean heart rate (HR) responses were significantly lower (p < 0.05) in the URN condition when compared to the RF condition. Furthermore, HR responses were statistically significantly affected by the height of the ceiling and the reach depth. Statistically significant differences (p< 0.05) in mean tidal volume (VT) occurred in the least (URN) and most (RF) restrictive conditions. Statistically significant differences (p < 0.05) in mean VE were evident between URN and URF, between URN and RF and between RN and RF. Ceiling height and reach demands had a statistically significant effect on all respiratory responses. There was a statistically significant difference in mean oxygen consumption (VO2) between the URN and all other conditions, and between the most restricted task (RF) and all other conditions. Both the effect of ceiling height and reach demands had a statistically statistically significant impact on VO2. Respiratory quotient (RQ) was significantly higher when loads were moved over 800mm compared to 400mm yet ceiling height did not have a statistically significant effect on RQ. Mean energy expenditure was significantly higher in the RF condition compared to the two least restrictive conditions (URN and RN). Statistically significant differences in EE were also evident between URN and RN, and between URN and URF. EE was significantly affected by reductions in ceiling height and increases in reach demands. Perceptually, the RF task (mean ‘Central’ RPE of 11) was perceived to place significantly greater cardiorespiratory demands on the operator compared to the URN (CRPE: 10) and RN (CRPE: 10) conditions. Statistically significant differences in perceived musculoskeletal strain only occurred between URN and RF. The effect of reach was perceived to have a statistically significant effect on both cardiovascular and musculoskeletal demands whereas ceiling height only had a statistically significant effect on musculoskeletal demands. The greatest discomfort was experienced in the lower back with the most intense discomfort occurring in the RN condition.