Inom områden som idrottsvetenskap och underhållning kan det finnas behov av att analysera en människas kroppsposition i 3D. Dessa behov kan innefatta att analysera en golfsving eller att möjliggöra mänsklig interaktion med spel. För att tillförlitligt uppskatta kroppspositioner krävs det idag specialiserad hårdvara som ofta är dyr och svårtillgänglig. På senare tid har det även tillkommit inlärningsbaserade lösningar som kan utföra samma uppskattning på vanliga bilder. Syftet med arbetet har varit att identifiera och jämföra populära inlärningsbaserade lösningar samt undersöka om någon av dessa presterar i paritet med en etablerad hårdvarubaserad lösning. För detta har testverktyg utvecklats, positionsuppskattningar genomförts och resul- tatdata för samtliga tester analyserats. Resultatet har visat att lösningarna inte pre- sterar likvärdigt med Kinect och att de i nuläget inte är tillräckligt välutvecklade för att användas som substitut för specialiserad hårdvara. / In fields such as sports science and entertainment, there’s occasionally a need to an- alyze a person's body pose in 3D. These needs may include analyzing a golf swing or enabling human interaction with games. Today, in order to reliably perform a human pose estimation, specialized hardware is usually required, which is often expensive and difficult to access. In recent years, multiple learning-based solutions have been developed that can perform the same kind of estimation on ordinary images. The purpose of this report has been to identify and compare popular learning-based so- lutions and to investigate whether any of these perform on par with an established hardware-based solution. To accomplish this, tools for testing have been developed, pose estimations have been conducted and result data for each test have been ana- lyzed. The result has shown that the solutions do not perform on par with Kinect and that they are currently not sufficiently well-developed to be used as a substitute for specialized hardware.

Submitted by Sara Ribeiro (sara.ribeiro@ucb.br) on 2017-11-16T13:26:59Z No. of bitstreams: 1 PierreSoaresBrand??oTese2017.pdf: 7621614 bytes, checksum: 642aeec63f324573dedd42645d955ac6 (MD5) / Approved for entry into archive by Sara Ribeiro (sara.ribeiro@ucb.br) on 2017-11-16T13:27:14Z (GMT) No. of bitstreams: 1 PierreSoaresBrand??oTese2017.pdf: 7621614 bytes, checksum: 642aeec63f324573dedd42645d955ac6 (MD5) / Made available in DSpace on 2017-11-16T13:27:14Z (GMT). No. of bitstreams: 1 PierreSoaresBrand??oTese2017.pdf: 7621614 bytes, checksum: 642aeec63f324573dedd42645d955ac6 (MD5) Previous issue date: 2017-08-23 / *** / A imagem corporal (IC) consiste na percep????o que uma pessoa tem sobre a dimens??o/tamanho e a forma do seu corpo. Embora exista um grande n??mero de instrumentos para avalia????o da IC, muitos estudos utilizam medidas n??o validadas para a popula????o brasileira e muitos instrumentos, embora de f??cil aplica????o, t??m complicados procedimentos de an??lise e interpreta????o dos resultados. O Microsoft Kinect ?? um sensor de movimento que tem sido utilizado n??o apenas em jogos de videogames, mas tamb??m adaptado para diferentes contextos e aplica????es na ??rea da sa??de. Neste sentido, partiu-se da possibilidade de utiliza????o do sensor Kinect para adapta????o de dois instrumentos de avalia????o da IC, tornando-os l??dicos e eficientes na apresenta????o dos resultados da avalia????o, e para o estabelecimento do objetivo geral de analisar a validade e a confiabilidade de sistema digital utilizando o Kinect para avaliar a IC de escolares de 10 a 12 anos em compara????o a m??todos tradicionais. Utilizou-se de uma pesquisa descritiva, quantitativa, por estudo tipo teste-reteste aplicado com 50 crian??as de uma escola particular do munic??pio de Palmas-TO, no per??odo de agosto de 2016 e mar??o de 2017. Como instrumentos de coleta de dados, utilizou-se o Image Marking Procedure (IMP) e a Escala de Silhuetas (ES), ambos os testes com uma vers??o digital utilizando o Kinect e outra tradicional. Foram analisadas as diferen??as intra e entre m??todos e entre avaliadores pela aplica????o do teste t de Student para amostras emparelhadas, o erro padr??o da m??dia (EPM), o coeficiente de correla????o de Pearson (r), o coeficiente de correla????o intraclasse (ICC), o erro padr??o de estimativa (EPE), a m??nima mudan??a detect??vel (MMD) para intervalo de confian??a de 95% (IC95%) e an??lise de Bland e Altman. Al??m de n??o apresentar diferen??as significativas em rela????o a vers??o tradicional da ES, a vers??o digital apresentou correla????o e confiabilidade entre boa a excelente, respectivamente r e ICC > 0,90 intra m??todo, r e ICC > 0,80 entre m??todos e r e ICC > 0,90 entre avaliadores. J?? o IMP apresentou diferen??a significativa (p<0,01) entre as vers??es tradicional e digital, al??m de correla????o e confiabilidade, respectivamente, pobres intra m??todo tradicional (r e ICC < 0,05), moderada intra m??todo digital (r e ICC entre 0,05 e 0,74), pobres entre avaliadores no m??todo tradicional (r e ICC < 0,05) e moderada entre avaliadores no m??todo digital (r e ICC entre 0,05 e 0,74). O m??todo digital de avalia????o da imagem corporal pela escala de silhuetas (ES) mostrou-se confi??vel e v??lido para a avalia????o tanto da percep????o quanto da satisfa????o corporal, al??m de ser eficiente na determina????o do ??ndice de massa corporal (IMC) da silhueta atual (SA) e do IMC da silhueta desejada (SD). O mesmo n??o foi observado no IMP tradicional e digital, que devem ser utilizados com cautela ou, preferencialmente, substitu??dos por outro instrumento.

This thesis will introduce the concept of collaboratively using freehand gestures to interact with visualizations. It could be problematic to work with data and visualizations together with others in the traditional desktop setting because of the limited screen size and a single user input device. Therefore this thesis suggests a solution by integrating computer vision and gestures with interactive visualizations. This integration resulted in a prototype where multiple users can interact with the same visualizations simultaneously. The prototype was evaluated and tested on ten potential users. The results from the tests show that using gestures have potential to support collaboration while working with interactive visualizations. It also shows what components are needed in order to enable gestural interaction with visualizations.

This diploma thesis deals with controlling computers by gestures. It contains a description of motion capture and available technologies, a more detailed description of the Microsoft Kinect sensor and a summary of available libraries for developing applications controlled by Kinect. Part of this thesis is also a list of projects from various areas that are working with Kinect. The output of this thesis is an application controllable by natural hand gestures, to allow common tasks such as selecting elements, browsing through images, etc. Data from the Kinect sensor are processed through the Microsoft Kinect SDK. The application is implemented in C#.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Shing I. Chang / This research aims to quantify human walking patterns through depth cameras to (1) detect walking pattern changes of a person with and without a motion-restricting device or a walking aid, and to (2) identify distinct walking patterns from different persons of similar physical attributes. Microsoft Kinect™ devices, often used for video games, were used to provide and track coordinates of 25 different joints of people over time to form a human skeleton. Two main studies were conducted. The first study aims at deciding whether motion-restricted devices such as a knee brace, an ankle brace, or walking aids – walkers or canes affect a person’s walking pattern or not. This study collects gait data from ten healthy subjects consisting of five females and five males walking a 10-foot path multiple times with and without motion-restricting devices. Their walking patterns were recorded in a form of time series via two Microsoft Kinect™ devices through frontal and sagittal planes. Two types of statistics were generated for analytic purposes. The first type is gait parameters converted from Microsoft Kinect™ coordinates of six selected joints. Then Sample Entropy (SE) measures were computed from the gait parameter values over time. The second method, on the other hand, applies the SE computations directly on the raw data derived from Microsoft Kinect™ devices in terms of (X, Y, Z) coordinates of 15 selected joints over time. The SE values were then used to compare the changes in each joint with and without motion-restricting devices. The experimental results show that both types of statistics are capable of detecting differences in walking patterns with and without motion-restricting devices for all ten subjects. The second study focuses on distinguishing two healthy persons with similar physical conditions. SE values from three gait parameters were used to distinguish one person from another via their walking patterns. The experimental results show that the proposed method using a star glyph summarizing the shape produced by the gait parameters is capable of distinguishing these two persons. Then multiple machine learning (ML) models were applied to the SE datasets from ten college-age subjects - five males and five females. In particular, ML models were applied to classify subjects into two categories: normal walking and abnormal walking (i.e. with motion-restricting devices). The best ML model (K-nearest neighborhood) was able to predict 97.3% accuracy using 10-fold cross-validation. Finally, ML models were applied to classify five gait conditions: walking normally, walking while wearing the ankle brace, walking while wearing the ACL brace, walking while using a cane, and walking while using a walker. The best ML model was again the K-nearest neighborhood performing at 98.7% accuracy rate.

[pt] Na atualidade, a avaliação do estado motor de pacientes com AVC e idosos é realizada mediante o uso de escalas qualitativas e sem padronização ou instrumentos de medição. As escalas são mais comuns por serem relativamente baratas e acessíveis, mas sofrem a desvantagem de serem subjetivas, variáveis, e requerem tempo de treinamento prolongado. Por outro lado, os instrumentos de avaliação, embora mais precisos e objetivos, têm o problema de serem heterogêneos, geralmente muito caros, e focados em objetivos específicos. O surgimento nos últimos tempos de sensores 3D com alta precisão e baixo custo, alguns deles bem conhecidos, como o Microsoft Kinect, permite a utilização de análise de movimento para quantificar o déficit ou sucesso de um tratamento fisioterapêutico ou medicamentoso, de forma quantitativa e padronizada, possibilitando também a comparação de forma automática com padrões de pessoas sãs, do mesmo estágio de doença, ou de caraterísticas similares. O objetivo desse trabalho é criar um sistema usando o Microsoft Kinect para a captura e processamento do estado motor de forma não-invasiva, fornecendo feedback clínico que permita realizar uma avaliação quantitativa e objetiva de pacientes com mobilidade reduzida, permitindo um acompanhamento da evolução da doença e redução do tempo de reabilitação. / [en] The evaluation of the motor status of stroke patients and elderly people is done by using qualitative scales without standardization or measuring instruments. The scales are most common because they are relatively inexpensive and accessible, but suffer the disadvantage of being subjective, variable, and require prolonged training time. Moreover, assessment instruments, although more accurate and objective, have the problem of being heterogeneous, usually very expensive, and focused on specific goals. The rise in recent times of 3D sensors with high accuracy and low cost, some of them well known as the Microsoft Kinect, allows the use of motion analysis to quantify the deficit or success of a physiotherapeutic or drug treatment in a quantitative and standardized way, enabling the automatic comparison with standards of healthy people, and people with the same stage of disease, or similar characteristics. The aim of this work is to create a system using Microsoft Kinect for capturing and processing motor status of patients with reduced mobility in a non-invasive way, providing clinical feedback that allows the conduction of a quantitative and objective evaluation of patients, enabling monitoring of disease progression and reduced rehabilitation time.

This diploma thesis deals with application of Microsoft Kinect sensor for remote control of mobile robot. The aim was design and implement methods for remote control of mobile robot with using of predefined gestures and these method practically tested.

No description available.

No description available.

The Balance Error Scoring System (BESS) test is a commonly used tool for assessing static postural stability after concussion that quantifies compensatory arm, eye and trunk movements. However, since it is scored by clinician observation, it is potentially susceptible to biased and inaccurate test scores. It is further limited by the need for properly trained clinicians to simultaneously administer, score and interpret the test. Such personnel may not always be available when concussion testing is needed such as at amateur sporting events or in military field situations. In response, we are creating a system to automatically administer and score the BESS in field conditions. The system is based on the Microsoft Kinect, which is an inexpensive commodity motion capture system originally developed for gaming applications. The Kinect can be interfaced to a custom-programmed laptop computer in order to quantitatively measure patient posture compensations for preventing balance loss such as degree of hip abduction/flexion, heel lift, and hand movement. By (a) removing the need for an adequately trained clinician, and (b) using rugged off-the-shelf system components, it will be possible to administer concussion assessments outside of standard clinical settings. Future work will determine whether the system can reduce score variability between clinicians. / Bioengineering