The main topic of the master thesis is the exploration of a process of learning and acquitision of skills. In fact these processes are the important parts of a human life indeed. The issue of this topic is being considered through a different but certainly related topic - the rules and their function. The first part of this thesis consists of the analyses of several points of view on the topic of rules as a whole. The introduction of the thesis is supplemented with the theory of S. Blackmore speaking about the meaning of mems in our lifes. After the first part, where the work of J. Peregrin "A Man and rules" is analised, the other bigger charter begins with the analysis of M. Merleau-Ponty point of view on a human body and embodiment - the essence of a human being. In fact the studies of S. Curtiss and A. S. Benzaquen are the sources of the particular cases that are closely connected with the topic of the previous chapters of the thesis and that close the thesis.

The idea that dance can be understood as an act of communication and a form of language has been already taken into account by scholars. The hypothesis that will be discussed in this MA dissertation concerns a more specific matter: a semiotic approach to different forms of dance improvisation, and notably the method traditionally labeled "intuitive dance". To understand this phenomena two main concepts will be conveyed: that of "quotation" understood via W. Benjamin's essays on Brecht, and that of "notation", as defined by N. Goodman in his Languages of art. Can we understand dance as a language - id est a quotable and notable code - even in its more intuitive forms? How is it possible to "understand", "quote" and "address" gestures, even in front of a wide heterogeneous audience and without any prefixed choreography but only on the base of a free and in-time creating process? Can we understand improvisation as a complex code? what and how does this code mean? Keywords: improvisation, Intuitive Dance, semiotics, notation, gesture, Nelson Goodman, Walter Benjamin, dance, code

The aim of the present study was to examine receptive and expressive lexicon, as well as gesture production among children with and without developmental language disorder (DLD), using the Swedish translation of the Picture Naming Game (PiNG). The parental evaluation Swedish Communicative Development Inventory III (SCDI III) was used to determine whether the results from PiNG and SCDI III were consistent. In this study, 10 children with DLD (study group) and 11 age-matched children with typical language development (control group) between 52 and 70 months of age participated. The participants were tested with PiNG, which is a test that examines reception and production of single nouns, verbs, adjectives, adverbs and prepositions. The guardians then completed SCDI III, which contains questions about the language abilities of the child. Gesture production in the form of deictic, iconic and conventional gestures was examined in conjunction with the use of PiNG. The results showed lower results for the study group on PiNG and SCDI III in comparison with the control group. The study group displayed a higher number of gestures than the control group, and in the former group, the use of gestures was positively correlated with the results from PiNG. In the study group, iconic gestures were used to compensate for language difficulties. The results from PiNG and SCDI III were not consistent. The age of the participants problably did not affect any results. The results from the present study indicate that PiNG may be useful when examining lexicon and gesture production among children with DLD. Both groups achieved high results on PiNG which may have affected the use of gestures and also possible correlations. For future studies, it is therefore recommended to apply PiNG on a group of younger children with DLD.

The dissertation undertakes an extensive investigation of the role of the gesture – from Lessing to emoji. Through close readings of bodily gestures as inscribed in text, documented on film, employed in performance, and shared throughout the cyberspace, the dissertation demonstrates how the human body has been imagined, conceptualized, and disciplined at various points since the second half of the 18th century. Presenting a reading of the body through the lens of different media, the analyses bring forth moments of disidentification and friction between medium and body: be that in gestural disobedience to ordered stage instructions, in resistance to the demands of the filmic apparatus, or in the form of a non-white emoji. To extrapolate historical developments and also processes of quotation and transference across media, material from different periods and disciplines is assembled: from unpublished manuscripts of the early Enlightenment (G. E. Lessing) via filmic footage from the late Weimar period (G. W. Pabst), to post-dramatic theatre performances around 2000 (Chr. Schlingensief), all the way up to present-day exchanges on social media platforms.

The thesis focuses on the fusion of theoretical knowledge of visual art and dance art. The practical use of this knowledge has resulted in the realization of presented dance choreographies which are inspired by non-traditional artistic forms such as performance or happening, and are using their methods. The key point of the whole thesis is the movement, which is seen as a crucial part of the dance and visual arts, as well as a primary essence of the human existence. My work also shows how the theme of movement can be used when working with children. In various etudes with children, it includes various art techniques (drawing, painting, spatial creation). The practical part focuses on the expression of human emotions through the specific movements of the body, in the form of dance choreographies. It also uses new technologies, where photographs and video footage serve as an output. At the same time, the thesis shows the process of collaboration with other artists and necessary communication with them, which represents one of the crucial parts of the origin of an art work and one important chapter of this work as well. Taking account of specifics of both attitudes the aim of my thesis was to connect the fundamental components of visual art and dance which has together created a new perspective and...

>Magister Scientiae - MSc / This thesis presents a system for performing whole gesture recognition for South African Sign Language. The system uses feature vectors combined with Hidden Markov models. In order to construct a feature vector, dynamic segmentation must occur to extract the signer's hand movements. Techniques and methods for normalising variations that occur when recording a signer performing a gesture, are investigated. The system has a classification rate of 69%.

Biometriska lösningar för åtkomstkontroll är ett blomstrande koncept. Precise Biometrics är ett företag som fokuserar på just biometriska lösningar relaterade till åtkomstkontroll. YOUNiQ är en produkt som fokuserar på ansiktsigenkänning. Denna produkt använder ansiktsigenkännig för att ge åtkomst till registrerade användare i systemet. Ett problem som uppstår vid att använda ansiktsigenkänning är att alla som befinner sig tillräckligt nära kameran blir skannade, även de som inte är registrerade. Denna avhandlingen har som mål att implementera ett avsiktsmedvetet system som använder en utlösare för att starta ett system. Istället för att använda ansiktsigenkänning på alla individer använder systemet gester som en utlösare för att starta systemet. Denna avhandlingen fokuserar inte på ansiktsigenkännning utan istället på utlösaren för att starta en process. Utvecklingsfasen sker i form utav en iterativ process för att skapa en prototyp. För att utvärdera systemet utfördes testfall för varje gest som är inkluderat i systemet. Efter testfallen var färdigställda sattes dem i ett verkligt scenario för att simulera en komplett interaktion med systemet. Utvärderingen användes sedan för att bestämma och vägleda för implementationen av ett avsiktsmedvetet system. Denna implementation kan ses som en signal till underliggande funktioner för att extrahera biometrisk data för till exempel ansiktsigenkänning. / Biometric solutions for access control is a thriving concept, Precise Biometrics is a company that focuses on just that. YOUNiQ is a product that focuses on facial identification for access control, with it comes an issue in where every person's face is being identified. This means identifying people that do not want to use the facial identification module. This thesis focuses on implementing an intent-aware system, a system which uses a trigger to begin a process. This thesis was done in collaboration with engineers at Precise Biometrics. Instead of identifying faces without permission the intent-aware system uses a trigger based on different hand gestures to begin the process. This thesis does not focus on face identification but instead the trigger before a specific process begins. The development phase consisted of an iterative process in creating the prototype system. In order to evaluate the system, test cases were done to verify accuracy of each hand gesture. Thereafter, a scenario was created to simulate an activation of the prototype system. The evaluation was used to determine the convenience and guidance when implementing intent-aware systems. Furthermore, the system can be seen as a form of trigger to allow for extracting biometric data in for example face identification.

Cette thèse a pour but d'améliorer la tâche de reconnaître des gestes de main en utilisant des techniques d'apprentissage par ordinateur et de traitement du signal. Les principales contributions de la thèse sont dédiés à la théorie de l'apprentissage par ordinateur et à l'interaction homme-machine. L'objectif étant d'implanter toutes méthodes en temps réel, toute méthode employé au cours de cette thèse était un compromis entre puissance et temps de calcul nécessaire.Plusieurs pistes ont été poursuivi : au début, la fusion des informations fournies par plusieurs capteurs tu type « time-of-flight » a été étudiée, dans le but d'améliorer le taux de reconnaissances correctes par rapport au cas avec un seul capteur. En particulier, l'impact des différentes caractéristiques calculés à partir d'une nuage de points, et de ses paramètres, a été évalué. Egalement, la performance des réseaux multi-couches (MLP) à été comparé avec celle d'un séparateur à vaste marge (SVM).En s'appuyant sur ces résultats, l'implantation du système dans une voiture a eté effectuée. Tout d'abord, nous avons montré que le système n'est pas du tout gêné par le fait d'être exposé aux conditions d'éclairage « outdoor ». L'extension de la base d'entraînement et une modification des caractéristiques calculé de la nuage des points a pu augmenter le taux de bonnes reconnaissances de façon très significative, ainsi que le rajout des mesures de confiance à la classification.Afin d'améliorer la performance des classifieurs à la base des réseaux multi-couche (MLP), une nouvelle méthode assez simple a été mise au point ensuite. Cette méthode met au profit des informations déjà présentes dans la dernière couche du réseau. En combinant cette nouvelle approche avec une technique de fusion, le taux de bonnes reconnaissances est amélioré, et surtout pour le cas des échantillons « difficiles ». Ces résultats ont été analysés et comparés de façon approfondie en comparant des différentes possibilités de fusion dans un tel contexte. L'exploitation du fait que les données traitées dont des séquences, et qu'il y a par conséquent une cohérence temporelle dans des échantillons successifs, a également été abordée un utilisant les mêmes techniques de fusion. Un système de « infotainment » implanté sur un smartphone, qui utilise les techniques décrites ici, a également été réalisé.Dans un dernier temps, un modèle simplifié de la reconnaissance des gestes dynamiques a été proposé et validé dans un contexte applicatif. Il a été montré que un geste peut être défini de façon assez robuste par une pose initiale et une pose finale, qui sont classé par le système décrit ci-dessus. / This thesis aims at improving the complex task of hand gesture recognition by utilizing machine learning techniques to learn from features calculated from 3D point cloud data. The main contributions of this work are embedded in the domains of machine learning and in the human-machine interaction. Since the goal is to demonstrate that a robust real-time capable system can be set up which provides a supportive means of interaction, the methods researched have to be light-weight in the sense that descriptivity balances itself with the calculation overhead needed to, in fact, remain real-time capable. To this end several approaches were tested:Initially the fusion of multiple ToF-sensors to improve the overall recognition rate was researched. It is examined, how employing more than one sensor can significantly boost recognition results in especially difficult cases and get a first grasp on the influence of the descriptors for this task as well as the influence of the choice of parameters on the calculation of the descriptor. The performance of MLPs with standard parameters is compared with the performance of SVMs for which the parameters have been obtained via grid search.Building on these results, the integration of the system into the car interior is shown. It is demonstrated how such a system can easily be integrated into an outdoor environment subject to strongly varying lighting conditions without the need for tedious calibration procedures. Furthermore the introduction of a modified light-weight version of the descriptor coupled with an extended database significantly boosts the frame rate for the whole recognition pipeline. Lastly the introduction of confidence measures for the output of the MLPs allows for more stable classification results and gives an insight on the innate challenges of this multiclass problem in general.In order to improve the classification performance of the MLPs without the need for sophisticated algorithm design or extensive parameter search a simple method is proposed which makes use of the existing recognition routines by exploiting information already present in the output neurons of the MLPs. A simple fusion technique is proposed which combines descriptor features with neuron confidences coming from a previously trained net and proves that augmented results can be achieved in nearly all cases for problem classes and individuals respectively.These findings are analyzed in-depth on a more theoretical scale by comparing the effectiveness of learning solely on neural activities in the output layer with the previously introduced fusion approach. In order to take into account temporal information, the thesis describes a possible approach on how to exploit the fact that we are dealing with a problem within which data is processed in a sequential manner and therefore problem-specific information can be taken into account. This approach classifies a hand pose by fusing descriptor features with neural activities coming from previous time steps and lays the ground work for the following section of making the transition towards dynamic hand gestures. Furthermore an infotainment system realized on a mobile device is introduced and coupled with the preprocessing and recognition module which in turn is integrated into an automotive setting demonstrating a possible testing environment for a gesture recognition system.In order to extend the developed system to allow for dynamic hand gesture interaction a simplified approach is proposed. This approach demonstrates that recognition of dynamic hand gesture sequences can be achieved with the simple definition of a starting and an ending pose based on a recognition module working with sufficient accuracy and even allowing for relaxed restrictions in terms of defining the parameters for such a sequence.

abstract: The advent of commercial inexpensive sensors and the advances in information and communication technology (ICT) have brought forth the era of pervasive Quantified-Self. Automatic diet monitoring is one of the most important aspects for Quantified-Self because it is vital for ensuring the well-being of patients suffering from chronic diseases as well as for providing a low cost means for maintaining the health for everyone else. Automatic dietary monitoring consists of: a) Determining the type and amount of food intake, and b) Monitoring eating behavior, i.e., time, frequency, and speed of eating. Although there are some existing techniques towards these ends, they suffer from issues of low accuracy and low adherence. To overcome these issues, multiple sensors were utilized because the availability of affordable sensors that can capture the different aspect information has the potential for increasing the available knowledge for Quantified-Self. For a), I envision an intelligent dietary monitoring system that automatically identifies food items by using the knowledge obtained from visible spectrum camera and infrared spectrum camera. This system is able to outperform the state-of-the-art systems for cooked food recognition by 25% while also minimizing user intervention. For b), I propose a novel methodology, IDEA that performs accurate eating action identification within eating episodes with an average F1-score of 0.92. This is an improvement of 0.11 for precision and 0.15 for recall for the worst-case users as compared to the state-of-the-art. IDEA uses only a single wrist-band which includes four sensors and provides feedback on eating speed every 2 minutes without obtaining any manual input from the user. / Dissertation/Thesis / Doctoral Dissertation Computer Engineering 2019

This licentiate thesis examines mathematical activities in a preschool. More precisely, the aim is to create knowledge of how mathematical activities emerge and are constructed in children's interactions with each other and with their teachers. The empirical material consists of video recordings and field notes collected through participant observations during a six-month period in two preschool units for children 3-5 years old. Theoretically the study is based on an ethnomethodological (EM) and conversation analytic (CA) perspective. Video recordings were analyzed using conversation analytic methods, involving a close and detailed analysis of sources in situated mathematical activities. Through the use of an EM and CA perspective this study contributes with new theoretical and methodological approaches to research on mathematical activities in preschools. In the close analysis of children's actions in interaction, an active child with ideas, interests, and commitment emerges, a child who uses a variety of communicative resources when participating in mathematical activities. Whether it is the children or the teachers who initiate the activity the children are actively involved in the construction of the mathematical content. Geometric shapes and concepts as well as different aspects of children's number sense are a couple of the mathematical topics covered in the study. In the activities the childrens display knowledge of math verbally as well as with their bodies, something that is analyzed by using the concept of epistemic stance. The preschool teachers sometimes used occasions when children display specific knowledge as an educational resource for other children's learning. The study also shows that children as well as their teachers follow each other's initiatives in the activities. This means that children change and enlarge the mathematical content within the activities and that the teachers follow the children's initiative. Through this reciprocity the mathematical content of the activity is maintained.