IMPACT OF NUMBER OF ATTRIBUTES ON THE ACCURACY OF HUMAN MOTION CLASSIFICATION

The quality of the human motion data faces challenges in producing high classification accuracy in large data streams for essential knowledge discovery. This reflects the need to identify the key factors that affect the results of classification. Present studies merely focus on estimating joints, skeleton and motions of human activities. However, the effect of the number of attributes towards classification accuracies of human motion has not been discussed. Therefore, this paper is aimed at determining the amount of attributes that affect the qualities of human motion classification. The case studies involve simple locomotion activities: jumping, walking and running retrieved from the public available domain. The raw video data were transformed into numeric in the form of x and y-coordinates and rotation angles as to be tested from a single up to triple combinations of data attributes. The impact of the number of attributes on classification accuracy is evaluated via Bayes, Function, Lazy, Meta, Rule and Trees classifier algorithms supported by the WEKA tool. Results revealed that three attributes data gave the best classification performance with an average accuracy of 81.50%. The findings also revealed that the number of attribute is directly proportional to the classification accuracy of human motion data

Augmentieren von Personen in Monokularen Videodaten

When aiming at realistic video augmentation, i.e. the embedding of virtual, 3-dimensional objects into a scene's original content, a series of challenging problems has to be solved. This is especially the case when working with solely monocular input material, as important additional 3D information is missing and has to be recovered during the process, if necessary. In this work, I will present a semi-automatic strategy to tackle this task by providing solutions to individual problems in the context of virtual clothing as an example for realistic video augmentation. Starting with two different approaches for monocular pose and motion estimation, I will show how to build a 3D human body model by estimating detailed shape information as well as basic surface material properties. This information allows to further extract a dynamic illumination model from the provided input material. The illumination model is particularly important for rendering a realistic virtual object and adds a lot of realism to the final video augmentation. The animated human model is able to interact with virtual 3D objects and is used in the context of virtual clothing to animate simulated garments. To achieve the desired realism, I present an additional image-based compositing approach that realistically embeds the simulated garment into the original scene content. Combining the presented approaches provide an integrated strategy for realistic augmentation of actors in monocular video sequences.Unter der Zielsetzung einer realistischen Videoaugmentierung durch das Einbetten virtueller, dreidimensionaler Objekte in eine bestehende Videoaufnahme, gibt eine Reihe interessanter und schwieriger Problemen zu lösen. Besonders im Hinblick auf die Verarbeitung monokularer Eingabedaten fehlen wichtige räumliche Informationen, welche aus den zweidimensionalen Eingabedaten rekonstruiert werden müssen. In dieser Arbeit präsentiere ich eine halbautomatische Verfahrensweise, welche es ermöglicht, die einzelnen Teilprobleme einer umfassenden Videoaugmentierung nacheinander in einer integrierten Strategie zu lösen. Dies demonstriere ich am Beispiel von virtueller Kleidung. Beginnend mit zwei unterschiedlichen Ansätzen zur Posen- und Bewegungsrekonstruktion wird ein realistisches 3D Körpermodell eines Menschen erzeugt. Dazu wird die detaillierte Körperform durch ein geeignetes Verfahren approximiert und eine Rekonstruktion der Oberflächenmaterialen vorgenommen. Diese Informationen werden unter anderem dazu verwendet, aus dem Eingabevideo eine dynamische Szenenbeleuchtung zu rekonstruieren. Die Beleuchtungsinformationen sind besonders wichtig für eine realistische Videoaugmentierung, da gerade eine korrekte Beleuchtung den Realitätsgrad des virtuell generierten Objektes erhöht. Das rekonstruierte und animierte Körpermodell ist durch seinen Detailgrad in der Lage, mit virtuellen Objekten zu interagieren. Dies kommt besonders im Anwendungsfall von virtueller Kleidung zum tragen. Um den gewünschten Realitätsgrad zu erreichen, führe ich ein zusätzliches, bild-basiertes Korrekturverfahren ein, welches hilft, die finale Bildkomposition zu optimieren. Die Kombination aller präsentierter Teilverfahren bildet eine vollumfängliche Strategie zur Augmentierung von monokularem Videomaterial, die zur realistischen Simulation und Einbettung von virtueller Kleidung eines Schauspielers im Originalvideo verwendet werden kann

Hand tracking for clinical applications: validation of the Google MediaPipe Hand (GMH) and the depth-enhanced GMH-D frameworks

Accurate 3D tracking of hand and fingers movements poses significant challenges in computer vision. The potential applications span across multiple domains, including human-computer interaction, virtual reality, industry, and medicine. While gesture recognition has achieved remarkable accuracy, quantifying fine movements remains a hurdle, particularly in clinical applications where the assessment of hand dysfunctions and rehabilitation training outcomes necessitate precise measurements. Several novel and lightweight frameworks based on Deep Learning have emerged to address this issue; however, their performance in accurately and reliably measuring fingers movements requires validation against well-established gold standard systems. In this paper, the aim is to validate the handtracking framework implemented by Google MediaPipe Hand (GMH) and an innovative enhanced version, GMH-D, that exploits the depth estimation of an RGB-Depth camera to achieve more accurate tracking of 3D movements. Three dynamic exercises commonly administered by clinicians to assess hand dysfunctions, namely Hand Opening-Closing, Single Finger Tapping and Multiple Finger Tapping are considered. Results demonstrate high temporal and spectral consistency of both frameworks with the gold standard. However, the enhanced GMH-D framework exhibits superior accuracy in spatial measurements compared to the baseline GMH, for both slow and fast movements. Overall, our study contributes to the advancement of hand tracking technology, the establishment of a validation procedure as a good-practice to prove efficacy of deep-learning-based hand-tracking, and proves the effectiveness of GMH-D as a reliable framework for assessing 3D hand movements in clinical applications

Patterns in Motion - From the Detection of Primitives to Steering Animations

In recent decades, the world of technology has developed rapidly. Illustrative of this trend is the growing number of affrdable methods for recording new and bigger data sets. The resulting masses of multivariate and high-dimensional data represent a new challenge for research and industry. This thesis is dedicated to the development of novel methods for processing multivariate time series data, thus meeting this Data Science related challenge. This is done by introducing a range of different methods designed to deal with time series data. The variety of methods re ects the different requirements and the typical stage of data processing ranging from pre-processing to post- processing and data recycling. Many of the techniques introduced work in a general setting. However, various types of motion recordings of human and animal subjects were chosen as representatives of multi-variate time series. The different data modalities include Motion Capture data, accelerations, gyroscopes, electromyography, depth data (Kinect) and animated 3D-meshes. It is the goal of this thesis to provide a deeper understanding of working with multi-variate time series by taking the example of multi-variate motion data. However, in order to maintain an overview of the matter, the thesis follows a basic general pipeline. This pipeline was developed as a guideline for time series processing and is the first contribution of this work. Each part of the thesis represents one important stage of this pipeline which can be summarized under the topics segmentation, analysis and synthesis. Specific examples of different data modalities, processing requirements and methods to meet those are discussed in the chapters of the respective parts. One important contribution of this thesis is a novel method for temporal segmentation of motion data. It is based on the idea of self-similarities within motion data and is capable of unsupervised segmentation of range of motion data into distinct activities and motion primitives. The examples concerned with the analysis of multi-variate time series re ect the role of data analysis in different inter-disciplinary contexts and also the variety of requirements that comes with collaboration with other sciences. These requirements are directly connected to current challenges in data science. Finally, the problem of synthesis of multi-variate time series is discussed using a graph-based example and examples related to rigging or steering of meshes. Synthesis is an important stage in data processing because it creates new data from existing ones in a controlled way. This makes exploiting existing data sets and and access of more condensed data possible, thus providing feasible alternatives to otherwise time-consuming manual processing.Muster in Bewegung - Von der Erkennung von Primitiven zur Steuerung von Animationen In den letzten Jahrzehnten hat sich die Welt der Technologie rapide entwickelt. Beispielhaft für diese Entwicklung ist die wachsende Zahl erschwinglicher Methoden zum Aufzeichnen neuer und immer größerer Datenmengen. Die sich daraus ergebenden Massen multivariater und hochdimensionaler Daten stellen Forschung wie Industrie vor neuartige Probleme. Diese Arbeit ist der Entwicklung neuer Verfahren zur Verarbeitung multivariater Zeitreihen gewidmet und stellt sich damit einer großen Herausforderung, welche unmittelbar mit dem neuen Feld der sogenannten Data Science verbunden ist. In ihr werden ein Reihe von verschiedenen Verfahren zur Verarbeitung multivariater Zeitserien eingeführt. Die verschiedenen Verfahren gehen jeweils auf unterschiedliche Anforderungen und typische Stadien der Datenverarbeitung ein und reichen von Vorverarbeitung bis zur Nachverarbeitung und darüber hinaus zur Wiederverwertung. Viele der vorgestellten Techniken eignen sich zur Verarbeitung allgemeiner multivariater Zeitreihen. Allerdings wurden hier eine Anzahl verschiedenartiger Aufnahmen von menschlichen und tierischen Subjekte ausgewählt, welche als Vertreter für allgemeine multivariate Zeitreihen gelten können. Zu den unterschiedlichen Modalitäten der Aufnahmen gehören Motion Capture Daten, Beschleunigungen, Gyroskopdaten, Elektromyographie, Tiefenbilder ( Kinect ) und animierte 3D -Meshes. Es ist das Ziel dieser Arbeit, am Beispiel der multivariaten Bewegungsdaten ein tieferes Verstndnis für den Umgang mit multivariaten Zeitreihen zu vermitteln. Um jedoch einen Überblick ber die Materie zu wahren, folgt sie jedoch einer grundlegenden und allgemeinen Pipeline. Diese Pipeline wurde als Leitfaden für die Verarbeitung von Zeitreihen entwickelt und ist der erste Beitrag dieser Arbeit. Jeder weitere Teil der Arbeit behandelt eine von drei größeren Stationen in der Pipeline, welche sich unter unter die Themen Segmentierung, Analyse und Synthese eingliedern lassen. Beispiele verschiedener Datenmodalitäten und Anforderungen an ihre Verarbeitung erläutern die jeweiligen Verfahren. Ein wichtiger Beitrag dieser Arbeit ist ein neuartiges Verfahren zur zeitlichen Segmentierung von Bewegungsdaten. Dieses basiert auf der Idee der Selbstähnlichkeit von Bewegungsdaten und ist in der Lage, verschiedenste Bewegungsdaten voll-automatisch in unterschiedliche Aktivitäten und Bewegungs-Primitive zu zerlegen. Die Beispiele fr die Analyse multivariater Zeitreihen spiegeln die Rolle der Datenanalyse in verschiedenen interdisziplinären Zusammenhänge besonders wider und illustrieren auch die Vielfalt der Anforderungen, die sich in interdisziplinären Kontexten auftun. Schließlich wird das Problem der Synthese multivariater Zeitreihen unter Verwendung eines graph-basierten und eines Steering Beispiels diskutiert. Synthese ist insofern ein wichtiger Schritt in der Datenverarbeitung, da sie es erlaubt, auf kontrollierte Art neue Daten aus vorhandenen zu erzeugen. Dies macht die Nutzung bestehender Datensätze und den Zugang zu dichteren Datenmodellen möglich, wodurch Alternativen zur ansonsten zeitaufwendigen manuellen Verarbeitung aufgezeigt werden

Mathematical modelling and simulation of the foot with specific application to the Achilles tendon

In this thesis, the development of an anatomically meaningful musculoskeletal model of the human foot with specific application to the Achilles tendon is presented. An in vivo experimental method of obtaining parameter values for the mechanical characteristics of the Achilles tendon and the gastrocnemius muscle is presented incorporating a Hill-type muscle model. The incentive for this work has been to enable the prediction of movement with regard to Achilles tendon motion of healthy volunteers, in order to then compare it with the movement of a pathologic gait and help in preventing Achilles tendon injuries. There are relatively few mathematical models that focus on the characterisation of the human Achilles tendon as part of a muscle-tendon unit in the literature. The mechanical properties of the Achilles tendon and the muscles connected to the tendon are usually calculated or predicted from muscle-tendon models such as the Hill-type muscle models. A significant issue in model based movement studies is that the parameter values in Hill-type muscle models are not determined by data obtained from in vivo experiments, but from data obtained from cadaveric specimens. This results in a complication when those predictive models are used to generate realistic predictions of human movement dynamics. In this study, a model of the Achilles tendon-gastrocnemius muscle is developed, incorporating assumptions regarding the mechanical properties of the muscle fibres and the tendinous tissue in series. Ultrasound images of volunteers, direct measurements and additional mathematical calculations are used to determine the initial lengths of the muscle-tendon complex as well as the final lengths during specific movements of the foot and the leg to parameterise the model. Ground reaction forces, forces on specific joints and moments and angles for the ankle are obtained from a 3D motion capture system. A novel experimental marker placement for the Achilles tendon is developed and generated in the 3D motion capture system. Movement dynamics of the foot are described using Newton’s laws, the principle of superposition and a technique known as the method of sections. Structural identifiability analyses of the muscle model ensured that values for the model parameters could be uniquely determined from perfect noise free data. Simulated model dynamics are fitted to measured movements of the foot. Model values are obtained on an individual subject basis. Model validation is performed from the experimental data captured for each volunteer and from reconstruction of the movements of specific trajectories of the joints, muscles and tendons involved in those movements. The major output of this thesis is a validated model of the Achilles tendon-gastrocnemius muscle that gives specific parameters for any individual studied and provides an integral component in the ultimate creation of a dynamic model of the human body. A new approach that was introduced in this thesis was the coupling of the Achilles tendon force from the musculoskeletal model to the muscle-tendon model and the non-linearity approach studied through a motion capture system. This approach and the new Achilles tendon marker placement is to the best of the author's knowledge, novel in the field of muscle-tendon research