Human motion capture technology is a powerful tool for advancing the understanding of human motion biomechanics (Andriacchi and Alexander, 2000). This is most readily accomplished by applying retroreflective markers to a participant’s skin and tracking the position of the markers during motion. Skin and adipose tissue move independently of the underlying bone during motion creating error known as soft tissue artifact (STA), the primary source of error in human motion capture (Leardini et al., 2005). (Solav et al., 2014) proposed and (Solav et al., 2015) expanded the triangular Cosserat point element (TCPE) method to reduce the effect of STA on derived kinematics through application of a marker cluster analyzed as a set of triangular Cosserat point elements. This method also provides metrics for three different modes of STA. Here the updated TCPE method (Solav et al., 2015) was compared to the established point cluster (PC) method of (Andriacchi et al., 1998) and the marker position error minimizing Procrustes solution (PS) method of (Söderkvist and Wedin, 1993) in two implant-based simulations, providing quantitative measures of error, and standard gait analysis, providing qualitative comparisons of each method’s determined kinematics. Both of these experiments allowed the TCPE method to generate observed STA parameters, informing the efficacy of the simulation. The TCPE method’s performance was similar to the PS method’s in the implant simulations and in standard gait. The PC method’s results seemed to be affected by numerical instability: simulation trial errors were larger and standard gait results were only similar to the other methods’ in general terms. While the PS and TCPE results were comparable, the TCPE method’s physiological basis provided the added benefit of non-rigid behavior quantization through its STA parameters. In this study, these parameters were on the same order of v magnitude between the standard gait experiments and the simulations, suggesting that implant simulations could be valuable substitutes when invasive methods are not available.

No description available.

No description available.

From 2012 - 2015 a foundation for future research in Goodwin Hall was designed, tested,developed, and implemented through an instrumentation project supported by the College of Engineering at Virginia Polytechnic Institute and State University. This required the design and implementation of a distributed, networked, and synchronized data acquisition system along with supporting hardware and software capable of measuring 227 accelerometers placed in 129 locations throughout the building. This system will provide a platform for research into a variety of topics, including structural health monitoring, building dynamics, human motion, and energy harvesting. Additionally, the system will be incorporated into the education curriculum by providing real-world data and hardware for students to interact with. This thesis covers the contributions of the author to the project. / Master of Science

This work is to investigate the performance of two Kalman Filter Algorithms, namely Linear Kalman Filter and Extended Kalman Filter on control-based human motion prediction in a real-time teleoperation. The Kalman Filter Algorithm has been widely used in research areas of motion tracking and GPS-navigation. However, the potential of human motion prediction by utilizing this algorithm is rarely being mentioned. Combine with the known issue - the delay issue in today’s teleoperation services, the author decided to build a prototype of simple teleoperation model based on the Kalman Filter Algorithm with the aim of eliminated the unsynchronization between the user’s inputs and the visual frames, where all the data were transferred over the network. In the first part of the thesis, two types of Kalman Filter Algorithm are applied on the prototype to predict the movement of the robotic arm based on the user’s motion applied on a Haptic Device. The comparisons in performance among the Kalman Filters have also been focused. In the second part, the thesis focuses on optimizing the motion prediction which based on the results of Kalman filtering by using the smoothing algorithm. The last part of the thesis examines the limitation of the prototype, such as how much the delays are accepted and how fast the movement speed of the Phantom Haptic can be, to still be able to obtain reasonable predations with acceptable error rate.   The results show that the Extended Kalman Filter has achieved more advantages in motion prediction than the Linear Kalman Filter during the experiments. The unsynchronization issue has been effectively improved by applying the Kalman Filter Algorithm on both state and measurement models when the latency is set to below 200 milliseconds. The additional smoothing algorithm further increases the accuracy. More important, it also solves shaking issue on the visual frames on robotic arm which is caused by the wavy property of the Kalman Filter Algorithm. Furthermore, the optimization method effectively synchronizes the timing when robotic arm touches the interactable object in the prediction.   The method which is utilized in this research can be a good reference for the future researches in control-based human motion tracking and prediction. / Detta arbete fokuserar på att undersöka prestandan hos två Kalman Filter Algoritmer, nämligen Linear Kalman Filter och Extended Kalman Filter som används i realtids uppskattningar av kontrollbaserad mänsklig rörelse i teleoperationen. Dessa Kalman Filter Algoritmer har används i stor utsträckning forskningsområden i rörelsespårning och GPS-navigering. Emellertid är potentialen i uppskattning av mänsklig rörelse genom att utnyttja denna algoritm sällan nämnas. Genom att kombinera med det kända problemet – fördröjningsproblem i dagens teleoperation tjänster beslutar författaren att bygga en prototyp av en enkel teleoperation modell vilket är baserad på Kalman Filter algoritmen i syftet att eliminera icke-synkronisering mellan användarens inmatningssignaler och visuella information, där alla data överfördes via nätverket. I den första delen av avhandlingen appliceras både Kalman Filter Algoritmer på prototypen för att uppskatta rörelsen av robotarmen baserat på användarens rörelse som anbringas på en haptik enhet. Jämförelserna i prestandan bland de Kalman Filter Algoritmerna har också fokuserats. I den andra delen fokuserar avhandlingen på att optimera uppskattningar av rörelsen som baserat på resultaten av Kalman-filtrering med hjälp av en utjämningsalgoritm. Den sista delen av avhandlingen undersökes begräsning av prototypen, som till exempel hur mycket fördröjningar accepteras och hur snabbt den haptik enheten kan vara, för att kunna erhålla skäliga uppskattningar med acceptabel felfrekvens.   Resultaten visar att den Extended Kalman Filter har bättre prestandan i rörelse uppskattningarna än den Linear Kalman Filter under experimenten. Det icke-synkroniseringsproblemet har förbättrats genom att tillämpa de Kalman Filter Algoritmerna på både statliga och värderingsmodeller när latensen är inställd på under 200 millisekunder. Den extra utjämningsalgoritmen ökar ytterligare noggrannheten. Denna algoritm löser också det skakande problem hos de visuella bilder på robotarmen som orsakas av den vågiga egenskapen hos Kalman Filter Algoritmen. Dessutom effektivt synkroniserar den optimeringsmetoden tidpunkten när robotarmen berör objekten i uppskattningarna.   Den metod som används i denna forskning kan vara en god referens för framtida undersökningar i kontrollbaserad rörelse- spåning och uppskattning.

This thesis focuses on addressing the limitations of existing human motion prediction models by extending the prediction horizon to very long-term forecasts. The objective is to develop a model that achieves one of the best stable prediction horizons in the field, providing accurate predictions without significant error increase over time. Through the utilization of quantization based models our research successfully achieves the desired objective with the proposed aligned version of Mean Per Joint Position Error. The first of the two proposed models, an attention-based Vector Quantized Variational AutoEncoder, demonstrates good performance in predicting beyond conventional time boundaries, maintaining low error rates as the prediction horizon extends. While slight discrepancies in joint positions are observed, the model effectively captures the underlying patterns and dynamics of human motion, which remains highly applicable in real-world scenarios. Furthermore, our investigation into a correlation-based Vector Quantized Variational AutoEncoder, as an alternative to attention-based one, highlights the challenges in capturing complex relationships and meaningful patterns within the data. The correlation-based VQ-VAE’s tendency to predict flat outputs emphasizes the need for further exploration and innovative approaches to improve its performance. Overall, this thesis contributes to the field of human motion prediction by extending the prediction horizon and providing insights into model performance and limitations. The developed model introduces a novel option to consider when contemplating long-term prediction applications across various domains and sets the foundation for future research to enhance performance in long-term scenarios. / Denna avhandling fokuserar på att hantera begränsningarna i befintliga modeller för förutsägelse av mänskliga rörelser genom att utöka förutsägelsehorisonten till mycket långsiktiga prognoser. Målet är att utveckla en modell som uppnår en av de bästa stabila prognoshorisonterna inom området, vilket ger korrekta prognoser utan betydande felökning över tiden. Genom att använda kvantiseringsbaserade modeller uppnår vår forskning framgångsrikt det önskade målet med den föreslagna anpassade versionen av Mean Per Joint Position Error. Den första av de två föreslagna modellerna, en uppmärksamhetsbaserad Vector Quantized Variational AutoEncoder, visar goda resultat när det gäller att förutsäga bortom konventionella tidsgränser och bibehåller låga felfrekvenser när förutsägelsehorisonten förlängs. Även om små avvikelser i ledpositioner observeras, fångar modellen effektivt de underliggande mönstren och dynamiken i mänsklig rörelse, vilket förblir mycket tillämpligt i verkliga scenarier. Vår undersökning av en korrelationsbaserad Vector Quantized Variational AutoEncoder, som ett alternativ till en uppmärksamhetsbaserad sådan, belyser dessutom utmaningarna med att fånga komplexa relationer och meningsfulla mönster i data. Den korrelationsbaserade VQ-VAE:s tendens att förutsäga platta utdata understryker behovet av ytterligare utforskning och innovativa metoder för att förbättra dess prestanda. Sammantaget bidrar denna avhandling till området för förutsägelse av mänskliga rörelser genom att utöka förutsägelsehorisonten och ge insikter om modellens prestanda och begränsningar. Den utvecklade modellen introducerar ett nytt alternativ att ta hänsyn till när man överväger långsiktiga prediktionstillämpningar inom olika områden och lägger grunden för framtida forskning för att förbättra prestanda i långsiktiga scenarier.

Der Einsatz digitaler Menschmodelle in der Produkt- und Prozessergonomie hat in den letzten Jahren beständig zugenommen. Vor allem Anforderungen aus dem industriellen Umfeld setzten hohe Maßstäbe an Schnelligkeit, Genauigkeit und Verlässlichkeit der verwendeten Systeme, Methoden und Verfahren. Das biomechanische Menschmodell Dynamicus ist eine am Institut für Mechatronik e.V., Chemnitz entwickelte Software, die sich auf dieses Gebiet der Simulation spezialisiert hat. Die Grundlage von Dynamicus-Simulationen sind reale menschliche Bewegungen, die mit Hilfe eines Motion-Capture-Systems aufgezeichnet werden. Die Analyse der digital vorliegenden Bewegungen erfolgt in den Wissenschaftsgebieten der Ergonomie, des Sports und der Rehabilitation.

Artificial neural networks (ANNs) have been used successfully in various practical problems. Though extensive improvements on different types of ANNs have been made to improve their performance, each ANN design still experiences its own limitations. The existing digital human models are mature enough to provide accurate and useful results for different tasks and scenarios under various conditions. There is, however, a critical need for these models to run in real time, especially those with large-scale problems like motion prediction which can be computationally demanding. For even small changes to the task conditions, the motion simulation needs to run for a relatively long time (minutes to tens of minutes). Thus, there can be a limited number of training cases due to the computational time and cost associated with collecting training data. In addition, the motion problem is relatively large with respect to the number of outputs, where there are hundreds of outputs (between 500-700 outputs) to predict for a single problem. Therefore, the aforementioned necessities in motion problems lead to the use of tools like the ANN in this work. This work introduces new algorithms for the design of the radial-basis network (RBN) for problems with minimal available training data. The new RBN design incorporates new training stages with approaches to facilitate proper setting of necessary network parameters. The use of training algorithms with minimal heuristics allows the new RBN design to produce results with quality that none of the competing methods have achieved. The new RBN design, called Opt_RBN, is tested on experimental and practical problems, and the results outperform those produced from standard regression and ANN models. In general, the Opt_RBN shows stable and robust performance for a given set of training cases. When the Opt_RBN is applied on the large-scale motion prediction application, the network experiences a CPU memory issue when performing the optimization step in the training process. Therefore, new algorithms are introduced to modify some steps of the new Opt_RBN training process to address the memory issue. The modified steps should only be used for large-scale applications similar to the motion problem. The new RBN design proposes an ANN that is capable of improved learning without needing more training data. Although the new design is driven by its use with motion prediction problems, the consequent ANN design can be used with a broad range of large-scale problems in various engineering and industrial fields that experience delay issues when running computational tools that require a massive number of procedures and a great deal of CPU memory. The results of evaluating the modified Opt_RBN design on two motion problems are promising, with relatively small errors obtained when predicting approximately 500-700 outputs. In addition, new methods for constraint implementation within the new RBN design are introduced. Moreover, the new RBN design and its associated parameters are used as a tool for simulated task analysis. This work initiates the idea that output weights (W) can be used to determine the most critical basis functions that cause the greatest reduction in the network test error. Then, the critical basis functions can specify the most significant training cases that are responsible for the proper performance achieved by the network. The inputs with the most change in value can be extracted from the basis function centers (U) in order to determine the dominant inputs. The outputs with the most change in value and their corresponding key body degrees-of-freedom for a motion task can also be specified using the training cases that are used to create the network's basis functions.

Orientador: Prof. Dr. Marcos Duarte / Dissertação (mestrado) - Universidade Federal do ABC. Programa de Pós-Graduação em Engenharia Biomédica, 2016. / Atualmente os dispositivos de instrumentação biomecânica, sobretudo os sistemas de captura de movimento, são capazes de fornecer um grande número de dados. Seja para extrair informações para análise de diferenças entre grupos, para semi-automatização de procedimentos clínicos típicos de laboratórios de marcha ou para o desenvolvimento de sistemas inteligentes que fazem uso da informação do movimento, o reconhecimento de padrões de movimento é uma necessidade. Estes dados podem ser reduzidos a séries temporais, sendo assim estamos diante de um problema de mineração de dados de séries temporais. No entanto enquanto a maior parte das pesquisas se concentram em tarefas de mineração, o problema fundamental de como representar uma série temporal ainda não foi plenamente abordado até agora. Este projeto objetiva estudar representações simbólicas para séries temporais provenientes de dados de captura de movimentos, uma abordagem ainda muito pouco explorada. Propondo a elaboração de um algoritmo que realiza o mapeamento entre um conjunto de séries temporais e uma sequência de símbolos, levando em consideração informações do domínio da biomecânica e controle motor. Um conjunto de movimentos discretos foram convertidos em uma representação simbólica, a partir da qual foi realizado um agrupamento hierárquico e classificação em 10 atividades rotuladas, com uma exatidão de 84.72% e um padrão sequencial foi detectado no andar. Estes resultados foram obtidos em um tempo de processamento relativamente baixo e a partir de apenas 3 ângulos no plano sagital do membro inferior direito. Com este trabalho atingimos o objetivo proposto e estabelecemos uma representação simbólica do movimento, denominada palavras do movimento, com boa parte das características almejadas. É uma representação simples e prática, a partir da qual foi possível estabelecer uma métrica que quantifica a similaridade entre movimentos. / Currently biomechanical instrumentation devices, especially motion capture systems, are able to provide a large amount of data. Be it for extract information to compare differences among different groups, for semi-automation of typical clinical gait analysis procedures or for developing intelligent systems that make use of motion information, recognition of motion patterns is a need. These data can be reduced to time series, so we are facing a problem of mining time series data. However, while most of the research communities have concentrated on the mining tasks, the fundamental problem on how to represent a time series has not yet been fully addressed so far. This project aims to study symbolic representation for time series data from motion capture, an approach still not much explored. The development of the algorithm that performs the mapping between a set of time series and a sequence of symbols, taking into account information from the field of biomechanics and motor control. A set of discrete motions were converted to a symbolic representation, from which we performed a hierarchical clustering and classified in 10 labeled activities, with an accuracy of 84.72% and a sequential pattern was detected in gait. These results were obtained at a relatively low processing time and from only 3 angles in the sagittal plane of the right lower limb. We achieved our objective and established a symbolic representation of the movement, called motion words, with most of it desired characteristics. It is a simple and practical representation, from which it was possible establish a metric that quantifies the similarity between movements.

Face aux limites auxquelles doivent faire face les systèmes optoélectroniques (matériel lourd, champ de mesure limité), les capteurs inertiels constituent une alternative prometteuse pour la mesure du mouvement humain. Grâce aux dernières avancées techniques, notamment en termes de miniaturisation des capteurs, leur utilisation en ambulatoire c’est-à-dire de façon autonome et embarquée est devenue possible. Mais ces opérations de miniaturisation ne sont pas sans effet sur les performances de ces capteurs. En effet, une telle mesure est dégradée par différents types de perturbations (stochastiques et déterministes) qui sont alors propagées au cours du processus dit de fusion des données visant à estimer l'orientation des segments humains. Classiquement, cette opération est réalisée à l'aide d'un filtre de Kalman dont le rôle est justement d'estimer une grandeur à partir d'une mesure bruitée en la confrontant à un modèle d'évolution.Dans ce contexte, nous proposons diverses méthodologies dans le but d'accéder à une mesure suffisamment précise pour être exploitée dans le cadre de l'analyse du mouvement humain. La première partie de cette thèse se focalise sur les capteurs. Tout d'abord, nous étudions les bruits de mesure issus des capteurs inertiels, puis nous leur attribuons un modèle afin de les prendre en compte au sein du filtre de Kalman. Ensuite, nous analysons les procédures de calibrage et évaluons leurs effets réels sur la mesure afin d'émettre quelques propositions en termes de compromis performance/facilité de réalisation.Dans une seconde partie, nous nous consacrons à l'algorithme de fusion des données. Après avoir proposé un filtre de Kalman adapté à la mesure du mouvement humain, nous nous focalisons sur un problème récurrent à ce stade : l'identification des matrices de covariance dont le rôle est d'attribuer une caractérisation globale aux erreurs de mesure. Cette méthode, basée sur une confrontation de la mesure avec une référence issue d'un système optoélectronique, met en évidence la nécessité de traiter ce problème rigoureusement.Dans une troisième partie, nous commençons à aborder les problèmes liés à l'utilisation des capteurs inertiels pour la mesure du mouvement humain, notamment le calibrage anatomique et le positionnement des capteurs.En conclusion, les gains apportés par les diverses propositions avancées dans cette thèse sont évalués et discutés. / To face the limits of optoelectronic systems (heavy device, restricted measurement field), inertial sensors are a promising alternative for human motion analysis. Thanks to the latest technical advancements like sensor miniaturization, they can now work autonomously which makes possible to directly embed them on the human segments. But, as a counterpart of these developments, inertial sensor measurement still suffers from both stochastic and deterministic perturbations. The induced errors then propagate over the so-called fusion algorithm used to estimate human segment orientation. A common tool to perform such an operation is the Kalman filter that estimates unknown variables by correcting noisy measurements by the use of a dynamic model.With the aim of achieving a sufficiently accurate measurement to perform human motion analysis, various methodologies are proposed in the present work. The first part of this thesis focuses on the sensors. First, inertial sensor noises are studied and modeled in order to be integrated into the Kalman filter. Calibration processes as their effects over the measurement are for that purposed analyzed. Some recommendations are thus proposed to reach a compromise between calibration performance and complexity.In a second part, the data fusion algorithm is approached. A specific Kalman filter dedicated to human motion measurement is first proposed. Then, a recurrent problem is studied in details: the definition of the covariance matrix that represents a globalcharacterization of the measurement errors. Considering an optoelectronic system as a reference to compare inertial measurement, a method is proposed for this covariance matrix identification, which also highlights the need to address this problem rigorously.In a third part, we begin to address the use of inertial sensors for human motion analysis by focusing on models and IMU-to-segment calibration.To conclude, the benefits made by the proposed methodologies are evaluated and discussed.