AutoGraff: towards a computational understanding of graffiti writing and related art forms.

The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes

Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)

[Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”. Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità, e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”. A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine. The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art. The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics

Note Taking in the Digital Age – Towards a Ubiquitous Pen Interface

The cultural technique of writing helped humans to express, communicate, think, and memorize throughout history. With the advent of human-computer-interfaces, pens as command input for digital systems became popular. While current applications allow carrying out complex tasks with digital pens, they lack the ubiquity and directness of pen and paper. This dissertation models the note taking process in the context of scholarly work, motivated by an understanding of note taking that surpasses mere storage of knowledge. The results, together with qualitative empirical findings about contemporary scholarly workflows that alternate between the analog and the digital world, inspire a novel pen interface concept. This concept proposes the use of an ordinary pen and unmodified writing surfaces for interacting with digital systems. A technological investigation into how a camera-based system can connect physical ink strokes with digital handwriting processing delivers artificial neural network-based building blocks towards that goal. Using these components, the technological feasibility of in-air pen gestures for command input is explored. A proof-of-concept implementation of a prototype system reaches real-time performance and demonstrates distributed computing strategies for realizing the interface concept in an end-user setting

Drawing, Handwriting Processing Analysis: New Advances and Challenges

International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline

Incorporating Human Expertise in Robot Motion Learning and Synthesis

With the exponential growth of robotics and the fast development of their advanced cognitive and motor capabilities, one can start to envision humans and robots jointly working together in unstructured environments. Yet, for that to be possible, robots need to be programmed for such types of complex scenarios, which demands significant domain knowledge in robotics and control. One viable approach to enable robots to acquire skills in a more flexible and efficient way is by giving them the capabilities of autonomously learn from human demonstrations and expertise through interaction. Such framework helps to make the creation of skills in robots more social and less demanding on programing and robotics expertise. Yet, current imitation learning approaches suffer from significant limitations, mainly about the flexibility and efficiency for representing, learning and reasoning about motor tasks. This thesis addresses this problem by exploring cost-function-based approaches to learning robot motion control, perception and the interplay between them. To begin with, the thesis proposes an efficient probabilistic algorithm to learn an impedance controller to accommodate motion contacts. The learning algorithm is able to incorporate important domain constraints, e.g., about force representation and decomposition, which are nontrivial to handle by standard techniques. Compliant handwriting motions are developed on an articulated robot arm and a multi-fingered hand. This work provides a flexible approach to learn robot motion conforming to both task and domain constraints. Furthermore, the thesis also contributes with techniques to learn from and reason about demonstrations with partial observability. The proposed approach combines inverse optimal control and ensemble methods, yielding a tractable learning of cost functions with latent variables. Two task priors are further incorporated. The first human kinematics prior results in a model which synthesizes rich and believable dynamical handwriting. The latter prior enforces dynamics on the latent variable and facilitates a real-time human intention cognition and an on-line motion adaptation in collaborative robot tasks. Finally, the thesis establishes a link between control and perception modalities. This work offers an analysis that bridges inverse optimal control and deep generative model, as well as a novel algorithm that learns cost features and embeds the modal coupling prior. This work contributes an end-to-end system for synthesizing arm joint motion from letter image pixels. The results highlight its robustness against noisy and out-of-sample sensory inputs. Overall, the proposed approach endows robots the potential to reason about diverse unstructured data, which is nowadays pervasive but hard to process for current imitation learning

Identification and Retraining of Sensorimotor Deficits to Reduce Intention Tremor in Multiple Sclerosis

Multiple sclerosis (MS) affects approximately 1 in 1000 Americans and is a significant cause of disability in the United States. One significant contributor to disability in MS is intention tremor, which manifests as an oscillation about the endpoint of a goal-directed movement. A major challenge of treating intention tremor is that the underlying causes of tremor in MS are unknown. In this study, we describe a systems-level computational model and an experimental technique that parameterizes subject-specific deficits in sensory feedback control during goal-directed movements. We used this approach to characterize sensorimotor control and examine how sensory and motor processes are differentially impacted by age and MS. The specific aims of this study were to: 1) characterize age-related changes in sensorimotor control during goal-directed movements; 2) characterize deficits in sensorimotor control in individuals with multiple sclerosis; and 3) determine whether sensorimotor control deficits can be modified and intention tremor reduced using robot-assisted therapy. We show that age-related changes in movement control can be ascribed to increases in sensory noise, leading to slower and less accurate movements. In persons with MS, changes in movement control associated with intention tremor can be attributed to increases in visual response delay that are unaccounted for by predictive neuromotor control mechanisms. Finally, we show that training of goal-directed movements using carefully selected feedback delays can enable subjects to adapt to their increased visual delay, thereby reducing system instability and tremor. The results demonstrate that systems identification techniques provide an informative framework for investigating how neuromotor disease affects motor control and for developing individually targeted rehabilitation strategies to reduce motor disability

Power Optimization for Sensor Hubs in Biomedical Applications

The design and development of wearable inertial sensor systems for health monitoring has garnered a huge attention in the scientific community and the industry during the last years. Such platforms have a typical architecture and common building blocks to enable data collection, data processing and feedback restitution. In this thesis we analyze power optimization techniques that can be applied to such systems. When reducing power consumption in a wearable system, different trade-offs have to be inevitably faced. We thus propose software techniques that span from well known duty cycling, frequency scaling, data compression to new paradigm such as radio triggering, heterogeneous multi-core and context aware power management

Rehabilitation Engineering

Population ageing has major consequences and implications in all areas of our daily life as well as other important aspects, such as economic growth, savings, investment and consumption, labour markets, pensions, property and care from one generation to another. Additionally, health and related care, family composition and life-style, housing and migration are also affected. Given the rapid increase in the aging of the population and the further increase that is expected in the coming years, an important problem that has to be faced is the corresponding increase in chronic illness, disabilities, and loss of functional independence endemic to the elderly (WHO 2008). For this reason, novel methods of rehabilitation and care management are urgently needed. This book covers many rehabilitation support systems and robots developed for upper limbs, lower limbs as well as visually impaired condition. Other than upper limbs, the lower limb research works are also discussed like motorized foot rest for electric powered wheelchair and standing assistance device