Development of a learning from demonstration environment using ZED 2i and HTC Vive Pro

Being able to teach complex capabilities, such as folding garments, to a bi-manual robot is a very challenging task, which is often tackled using learning from demonstration datasets. The few garment folding datasets available nowadays to the robotics research community are either gathered from human demonstrations or generated through simulation. The former have the huge problem of perceiving human action and transferring it to the dynamic control of the robot, while the latter requires coding human motion into the simulator in open loop, resulting in far-from-realistic movements. In this thesis, a novel virtual reality (VR) framework is proposed, based on Unity’s 3D platform and the use of HTC Vive Pro system, ZED mini, and ZED 2i cameras, and Leap motion’s hand-tracking module. The framework is capable of detecting and tracking objects, animals, and human bodies in a 3D environment. Moreover, the framework is also capable of simulating very realistic garments while allowing users to interact with them, in real-time, either through handheld controllers or the user’s real hands. By doing so, and thanks to the immersive experience, the framework gets rid of the gap between the human and robot perception-action loop, while simplifying data capture and resulting in more realistic samples. Finally, using the developed framework, a novel garment manipulation dataset will be recorded, containing samples with data and videos of nineteen different types of manipulation which aim to help tasks related to robot learning by demonstrationObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructur

Interactions gestuelles multi-point et géométrie déformable pour l’édition 3D sur écran tactile

Despite the advances made in the fields of existing objects capture and of procedural generation, creation of content for virtual worlds can not be perform without human interaction. This thesis suggests to exploit new touch devices ("multi-touch" screens) to obtain an easy, intuitive 2D interaction in order to navigate inside a virtual environment, to manipulate, position and deform 3D objects.First, we study the possibilities and limitations of the hand and finger gestures while interacting on a touch screen in order to discover which gestures are the most adapted to edit 3D scene and environment. In particular, we evaluate the effective number of degrees of freedom of the human hand when constrained on a planar surface. Meanwhile, we develop a new gesture analysis method using phases to identify key motion of the hand and fingers in real time. These results, combined to several specific user-studies, lead to a gestural design pattern which handle not only navigation (camera positioning), but also object positioning, rotation and global scaling. Then, this pattern is extended to complex deformation (such as adding and deleting material, bending or twisting part of objects, using local control). Using these results, we are able to propose and evaluate a 3D world editing interface that handle a naturaltouch interaction, in which mode selection (i.e. navigation, object positioning or object deformation) and task selections is automatically processed by the system, relying on the gesture and the interaction context (without any menu or button). Finally, we extend this interface to integrate more complex deformations, adapting the garment transfer from a character to any other in order to process interactive deformation of the garment while the wearing character is deformed.Malgré les progrès en capture d’objets réels et en génération procédurale, la création de contenus pour les mondes virtuels ne peut se faire sans interaction humaine. Cette thèse propose d’exploiter les nouvelles technologies tactiles (écrans "multi-touch") pour offrir une interaction 2D simple et intuitive afin de naviguer dans un environnement virtuel, et d’y manipuler, positionner et déformer des objets 3D.En premier lieu, nous étudions les possibilité et les limitations gestuelles de la main et des doigts lors d’une interaction sur écran tactile afin de découvrir quels gestes semblent les plus adaptés à l’édition des environnements et des objets 3D. En particulier, nous évaluons le nombre de degré de liberté efficaces d’une main humaine lorsque son geste est contraint à une surface plane. Nous proposons également une nouvelle méthode d’analyse gestuelle par phases permettant d’identifier en temps réel les mouvements clés de la main et des doigts. Ces résultats, combinés à plusieurs études utilisateur spécifiques, débouchent sur l’identification d’un patron pour les interactions gestuelles de base incluant non seulement navigation (placement de caméra), mais aussi placement, rotation et mise à l’échelle des objets. Ce patron est étendudans un second temps aux déformations complexes (ajout et suppression de matière ainsi que courbure ou torsion des objets, avec contrôle de la localité). Tout ceci nous permet de proposer et d’évaluer une interface d’édition des mondes 3D permettant une interaction tactile naturelle, pour laquelle le choix du mode (navigation, positionnement ou déformation) et des tâches correspondantes est automatiquement géré par le système en fonction du geste et de son contexte (sans menu ni boutons). Enfin, nous étendons cette interface pour y intégrer des déformations plus complexe à travers le transfert de vêtements d’un personnage à un autre, qui est étendu pour permettre la déformation interactive du vêtement lorsque le personnage qui le porte est déformé par interaction tactile

Practical color-based motion capture

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 93-101).Motion capture systems track the 3-D pose of the human body and are widely used for high quality content creation, gestural user input and virtual reality. However, these systems are rarely deployed in consumer applications due to their price and complexity. In this thesis, we propose a motion capture system built from commodity components that can be deployed in a matter of minutes. Our approach uses one or more webcams and a color garment to track either the user's upper body or hands for motion capture and user input. We demonstrate that custom designed color garments can simplify difficult computer vision problems and lead to efficient and robust algorithms for hand and upper body tracking. Specifically, our highly descriptive color patterns alleviate ambiguities that are commonly encountered when tracking only silhouettes or edges, allowing us to employ a nearest-neighbor approach to track either the hands or the upper body at interactive rates. We also describe a robust color calibration system that enables our color-based tracking to work against cluttered backgrounds and under multiple illuminants. We demonstrate our system in several real-world indoor and outdoor settings and describe proof-of-concept applications enabled by our system that we hope will provide a foundation for new interactions in computer aided design, animation control and augmented reality.by Robert Yuanbo Wang.Ph.D

Virtuaalse proovikabiini 3D kehakujude ja roboti juhtimisalgoritmide uurimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneVirtuaalne riiete proovimine on üks põhilistest teenustest, mille pakkumine võib suurendada rõivapoodide edukust, sest tänu sellele lahendusele väheneb füüsilise töö vajadus proovimise faasis ning riiete proovimine muutub kasutaja jaoks mugavamaks. Samas pole enamikel varem välja pakutud masinnägemise ja graafika meetoditel õnnestunud inimkeha realistlik modelleerimine, eriti terve keha 3D modelleerimine, mis vajab suurt kogust andmeid ja palju arvutuslikku ressurssi. Varasemad katsed on ebaõnnestunud põhiliselt seetõttu, et ei ole suudetud korralikult arvesse võtta samaaegseid muutusi keha pinnal. Lisaks pole varasemad meetodid enamasti suutnud kujutiste liikumisi realistlikult reaalajas visualiseerida. Käesolev projekt kavatseb kõrvaldada eelmainitud puudused nii, et rahuldada virtuaalse proovikabiini vajadusi. Välja pakutud meetod seisneb nii kasutaja keha kui ka riiete skaneerimises, analüüsimises, modelleerimises, mõõtmete arvutamises, orientiiride paigutamises, mannekeenidelt võetud 3D visuaalsete andmete segmenteerimises ning riiete mudeli paigutamises ja visualiseerimises kasutaja kehal. Selle projekti käigus koguti visuaalseid andmeid kasutades 3D laserskannerit ja Kinecti optilist kaamerat ning koostati nendest andmebaas. Neid andmeid kasutati välja töötatud algoritmide testimiseks, mis peamiselt tegelevad riiete realistliku visuaalse kujutamisega inimkehal ja suuruse pakkumise süsteemi täiendamisega virtuaalse proovikabiini kontekstis.Virtual fitting constitutes a fundamental element of the developments expected to rise the commercial prosperity of online garment retailers to a new level, as it is expected to reduce the load of the manual labor and physical efforts required. Nevertheless, most of the previously proposed computer vision and graphics methods have failed to accurately and realistically model the human body, especially, when it comes to the 3D modeling of the whole human body. The failure is largely related to the huge data and calculations required, which in reality is caused mainly by inability to properly account for the simultaneous variations in the body surface. In addition, most of the foregoing techniques cannot render realistic movement representations in real-time. This project intends to overcome the aforementioned shortcomings so as to satisfy the requirements of a virtual fitting room. The proposed methodology consists in scanning and performing some specific analyses of both the user's body and the prospective garment to be virtually fitted, modeling, extracting measurements and assigning reference points on them, and segmenting the 3D visual data imported from the mannequins. Finally, superimposing, adopting and depicting the resulting garment model on the user's body. The project is intended to gather sufficient amounts of visual data using a 3D laser scanner and the Kinect optical camera, to manage it in form of a usable database, in order to experimentally implement the algorithms devised. The latter will provide a realistic visual representation of the garment on the body, and enhance the size-advisor system in the context of the virtual fitting room under study

Towards gestural understanding for intelligent robots

Fritsch JN. Towards gestural understanding for intelligent robots. Bielefeld: Universität Bielefeld; 2012.A strong driving force of scientific progress in the technical sciences is the quest for systems that assist humans in their daily life and make their life easier and more enjoyable. Nowadays smartphones are probably the most typical instances of such systems. Another class of systems that is getting increasing attention are intelligent robots. Instead of offering a smartphone touch screen to select actions, these systems are intended to offer a more natural human-machine interface to their users. Out of the large range of actions performed by humans, gestures performed with the hands play a very important role especially when humans interact with their direct surrounding like, e.g., pointing to an object or manipulating it. Consequently, a robot has to understand such gestures to offer an intuitive interface. Gestural understanding is, therefore, a key capability on the way to intelligent robots. This book deals with vision-based approaches for gestural understanding. Over the past two decades, this has been an intensive field of research which has resulted in a variety of algorithms to analyze human hand motions. Following a categorization of different gesture types and a review of other sensing techniques, the design of vision systems that achieve hand gesture understanding for intelligent robots is analyzed. For each of the individual algorithmic steps – hand detection, hand tracking, and trajectory-based gesture recognition – a separate Chapter introduces common techniques and algorithms and provides example methods. The resulting recognition algorithms are considering gestures in isolation and are often not sufficient for interacting with a robot who can only understand such gestures when incorporating the context like, e.g., what object was pointed at or manipulated. Going beyond a purely trajectory-based gesture recognition by incorporating context is an important prerequisite to achieve gesture understanding and is addressed explicitly in a separate Chapter of this book. Two types of context, user-provided context and situational context, are reviewed and existing approaches to incorporate context for gestural understanding are reviewed. Example approaches for both context types provide a deeper algorithmic insight into this field of research. An overview of recent robots capable of gesture recognition and understanding summarizes the currently realized human-robot interaction quality. The approaches for gesture understanding covered in this book are manually designed while humans learn to recognize gestures automatically during growing up. Promising research targeted at analyzing developmental learning in children in order to mimic this capability in technical systems is highlighted in the last Chapter completing this book as this research direction may be highly influential for creating future gesture understanding systems

A proposal to improve wearables development time and performance : software and hardware approaches.

Programa de P?s-Gradua??o em Ci?ncia da Computa??o. Departamento de Ci?ncia da Computa??o, Instituto de Ci?ncias Exatas e Biol?gicas, Universidade Federal de Ouro Preto.Wearable devices are a trending topic in both commercial and academic areas. Increasing demand for innovation has raised the number of research and products, addressing brandnew challenges, and creating profitable opportunities. Current wearable devices can be employed in solving problems in a wide variety of areas. Such coverage generates a relevant number of requirements and variables that influences solutions performance. It is common to build specific wearable versions to fit each targeting application niche, what requires time and resources. Currently, the related literature does not present ways to treat the hardware/software in a generic way enough to allow both parts reuse. This manuscript presents the proposal of two components focused on hardware/software, respectively, allowing the reuse of di?erent parts of a wearable solution. A platform for wearables development is outlined as a viable way to recycle an existing organization and architecture. The platform use was proven through the creation of a wearable device that was enabled to be used in di?erent contexts of the mining industry. In the software side, a development and customization tool for specific operating systems is demonstrated. This tool aims not only to reuse standard software components but also to provide improved performance simultaneously. A real prototype was designed and created as a manner to validate the concepts. In the results, the comparison between the operating system generated by the tool versus a conventional operating system allows quantifying the improvement rate. The former operating system showed approximate performance gains of 100% in processing tasks, 150% in memory consumption and I/O operations, and approximately 20% of reduction in energy consumption. In the end, performance analysis allows inferring that the proposals presented here contribute to this area, easing the development and reuse of wearable solutions as a whole

Proceedings of the 1st joint workshop on Smart Connected and Wearable Things 2016

These are the Proceedings of the 1st joint workshop on Smart Connected and Wearable Things (SCWT'2016, Co-located with IUI 2016). The SCWT workshop integrates the SmartObjects and IoWT workshops. It focusses on the advanced interactions with smart objects in the context of the Internet-of-Things (IoT), and on the increasing popularity of wearables as advanced means to facilitate such interactions

Hybrid Wearable Signal Processing/Learning via Deep Neural Networks

Wearable technologies are gaining considerable attention in recent years as a potential post-smartphone platform with several applications of significant engineering importance. Wearable technologies are expected to become more prevalent in a variety of areas, including modern healthcare practices, robotic prosthesis control, Artificial Reality (AR) and Virtual Reality (VR) applications, Human Machine Interface/Interaction (HMI), and remote support for patients and chronically ill patients at home. The emergence of wearable technologies can be attributed to the advancement of flexible electronic materials; the availability of advanced cloud and wireless communication systems, and; the Internet of Things (IoT) coupled with high demand from the tech-savvy population and the elderly population for healthcare management. Wearable devices in the healthcare realm gather various biological signals from the human body, among which Electrocardiogram (ECG), Photoplethysmogram (PPG), and surface Electromyogram (sEMG), are the most widely non-intrusive monitored signals. Utilizing these widely used non-intrusive signals, the primary emphasis of the proposed dissertation is on the development of advanced Machine Learning (ML), in particular Deep Learning (DL), algorithms to increase the accuracy of wearable devices in specific tasks. In this context and in the first part, using ECG and PPG bio-signals, we focus on development of accurate subject-specific solutions for continuous and cuff-less Blood Pressure (BP) monitoring. More precisely, a deep learning-based framework known as BP-Net is proposed for predicting continuous upper and lower bounds of blood pressure, respectively, known as Systolic BP (SBP) and Diastolic BP (DBP). Furthermore, by capitalizing on the fact that datasets used in recent literature are not unified and properly defined, a unified dataset is constructed from the MIMIC-I and MIMIC-III databases obtained from PhysioNet. In the second part, we focus on hand gesture recognition utilizing sEMG signals, which have the potential to be used in the myoelectric prostheses control systems or decoding Myo Armbands data to interpret human intent in AR/VR environments. Capitalizing on the recent advances in hybrid architectures and Transformers in different applications, we aim to enhance the accuracy of sEMG-based hand gesture recognition by introducing a hybrid architecture based on Transformers, referred to as the Transformer for Hand Gesture Recognition (TraHGR). In particular, the TraHGR architecture consists of two parallel paths followed by a linear layer that acts as a fusion center to integrate the advantage of each module. The ultimate goal of this work is to increase the accuracy of gesture classifications, which could be a major step towards the development of more advanced HMI systems that can improve the quality of life for people with disabilities or enhance the user experience in AR/VR applications. Besides improving accuracy, decreasing the number of parameters in the Deep Neural Network (DNN) architectures plays an important role in wearable devices. In other words, to achieve the highest possible accuracy, complicated and heavy-weighted Deep Neural Networks (DNNs) are typically developed, which restricts their practical application in low-power and resource-constrained wearable systems. Therefore, in our next attempt, we propose a lightweight hybrid architecture based on the Convolutional Neural Network (CNN) and attention mechanism, referred to as Hierarchical Depth-wise Convolution along with the Attention Mechanism (HDCAM), to effectively extract local and global representations of the input. The key objective behind the design of HDCAM was to ensure its resource efficiency while maintaining comparable or better performance than the current state-of-the-art methods

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Intelligent ultrasound hand gesture recognition system

With the booming development of technology, hand gesture recognition has become a hotspot in Human-Computer Interaction (HCI) systems. Ultrasound hand gesture recognition is an innovative method that has attracted ample interest due to its strong real-time performance, low cost, large field of view, and illumination independence. Well-investigated HCI applications include external digital pens, game controllers on smart mobile devices, and web browser control on laptops. This thesis probes gesture recognition systems on multiple platforms to study the behavior of system performance with various gesture features. Focused on this topic, the contributions of this thesis can be summarized from the perspectives of smartphone acoustic field and hand model simulation, real-time gesture recognition on smart devices with speed categorization algorithm, fast reaction gesture recognition based on temporal neural networks, and angle of arrival-based gesture recognition system. Firstly, a novel pressure-acoustic simulation model is developed to examine its potential for use in acoustic gesture recognition. The simulation model is creating a new system for acoustic verification, which uses simulations mimicking real-world sound elements to replicate a sound pressure environment as authentically as possible. This system is fine-tuned through sensitivity tests within the simulation and validate with real-world measurements. Following this, the study constructs novel simulations for acoustic applications, informed by the verified acoustic field distribution, to assess their effectiveness in specific devices. Furthermore, a simulation focused on understanding the effects of the placement of sound devices and hand-reflected sound waves is properly designed. Moreover, a feasibility test on phase control modification is conducted, revealing the practical applications and boundaries of this model. Mobility and system accuracy are two significant factors that determine gesture recognition performance. As smartphones have high-quality acoustic devices for developing gesture recognition, to achieve a portable gesture recognition system with high accuracy, novel algorithms were developed to distinguish gestures using smartphone built-in speakers and microphones. The proposed system adopts Short-Time-Fourier-Transform (STFT) and machine learning to capture hand movement and determine gestures by the pretrained neural network. To differentiate gesture speeds, a specific neural network was designed and set as part of the classification algorithm. The final accuracy rate achieves 96% among nine gestures and three speed levels. The proposed algorithms were evaluated comparatively through algorithm comparison, and the accuracy outperformed state-of-the-art systems. Furthermore, a fast reaction gesture recognition based on temporal neural networks was designed. Traditional ultrasound gesture recognition adopts convolutional neural networks that have flaws in terms of response time and discontinuous operation. Besides, overlap intervals in network processing cause cross-frame failures that greatly reduce system performance. To mitigate these problems, a novel fast reaction gesture recognition system that slices signals in short time intervals was designed. The proposed system adopted a novel convolutional recurrent neural network (CRNN) that calculates gesture features in a short time and combines features over time. The results showed the reaction time significantly reduced from 1s to 0.2s, and accuracy improved to 100% for six gestures. Lastly, an acoustic sensor array was built to investigate the angle information of performed gestures. The direction of a gesture is a significant feature for gesture classification, which enables the same gesture in different directions to represent different actions. Previous studies mainly focused on types of gestures and analyzing approaches (e.g., Doppler Effect and channel impulse response, etc.), while the direction of gestures was not extensively studied. An acoustic gesture recognition system based on both speed information and gesture direction was developed. The system achieved 94.9% accuracy among ten different gestures from two directions. The proposed system was evaluated comparatively through numerical neural network structures, and the results confirmed that incorporating additional angle information improved the system's performance. In summary, the work presented in this thesis validates the feasibility of recognizing hand gestures using remote ultrasonic sensing across multiple platforms. The acoustic simulation explores the smartphone acoustic field distribution and response results in the context of hand gesture recognition applications. The smartphone gesture recognition system demonstrates the accuracy of recognition through ultrasound signals and conducts an analysis of classification speed. The fast reaction system proposes a more optimized solution to address the cross-frame issue using temporal neural networks, reducing the response latency to 0.2s. The speed and angle-based system provides an additional feature for gesture recognition. The established work will accelerate the development of intelligent hand gesture recognition, enrich the available gesture features, and contribute to further research in various gestures and application scenarios