A Block-based Programming Approach

Publisher Copyright: Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.Simple computer activities have been used for a long time for cognitive and physical training in the context of rehabilitation or mental stimulation. An example is training the skills needed by users with special needs to access the computer through scanning access, which is an access method used by persons with major motor difficulties. The increased availability of less expensive mobile devices with large displays provides an ideal platform for these applications. This work-in-progress paper presents an ongoing work to empower rehabilitation therapists and special education teachers with no previous computer programming experience with a set of highly configurable apps supporting several types of activities for scanning access training. The apps are made available as open projects written in a block-based programming language. This way, they may be configurable by non-programmers while also allowing further changes depending on the programming skills of each rehabilitation therapist or special education teacher. This study intends to validate this approach among this group of users and formulate a set of guidelines concerning software architecture and organization and user interaction, to be used in the development of this kind of application.publishersversionpublishe

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

Sistema de Deteção de Quedas Automático Baseado em Vídeo

The elderly population faces difficulties in completing certain tasks independently, often re quiring supervision to not only assist them but also to mitigate and notify about potential health risks. Falls, a prevalent and severe problem, pose a high risk of causing hospitaliza tions and fatalities. However, the aging population in developed countries is growing at an unprecedented rate, while the proportion of active age individuals continues to decline. Con sequently, elderly care has become less accessible as caregivers are confronted with a larger number of patients. Nonetheless, conventional fall detection methods, typically triggered by victims themselves, are unreliable and inadequate. This thesis proposes an automatic alternative to existing methods, presenting a computer vision-based Fall Detection System (FDS) that utilizes a two-stream Inflated 3D Convolutional Neural Network (I3D) in con junction with a Recurrent Neural Network (RNN). To enhance the available datasets, a new collection of simulated falls was created. Experimental evaluations demonstrate the superi ority of this hybrid model over state-of-the-art fall detection models, achieving an accuracy of 94% and a recall value of 96%. By promptly and accurately detecting falls, a system employing this model could significantly reduce the risk of severe injuries posed to the elderly and physically disabled individuals.Os idosos enfrentam dificuldades em completar certas tarefas sozinhos e precisam de su pervisão frequente, não só para assistí-los, mas também para mitigar e alertar para riscos potenciais de saúde. Quedas são problemas prevalentes e sérios, muitas vezes resultando em hospitalizações ou mortes. Contudo, nos países desenvolvidos, a população idosa está a crescer e a proporção de cidadãos de idade ativa a diminuir. Por consequência, cuidados a idosos tornam-se mais inacessíveis, já que enfermeiros são confrontados com um maior número de pacientes. Não obstante, métodos convencionais de deteção de quedas, que requerem, normalmente, a ativação por parte da vítima, não são confiáveis nem adequados. Esta tese propõe uma alternativa automática a estes métodos na forma de um sistema de deteção de quedas que incorpora uma rede neuronal convolucional 3D juntamente com uma rede neuronal recorrente. Para melhorar os datasets já existentes, uma nova coleção de vídeos de quedas foi criada. Este modelo híbrido revela ter performances superiores às de outros modelos, conseguindo uma acurácia de 94% e uma sensitividade de 96%. Ao ser capaz de detetar quedas precisa e imediatamente, um sistema que inclui este modelo poderá reduzir drasticamente o risco de ferimentos graves aos idosos e pessoas com deficiências físicas

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

When I Look into Your Eyes: A Survey on Computer Vision Contributions for Human Gaze Estimation and Tracking

The automatic detection of eye positions, their temporal consistency, and their mapping into a line of sight in the real world (to find where a person is looking at) is reported in the scientific literature as gaze tracking. This has become a very hot topic in the field of computer vision during the last decades, with a surprising and continuously growing number of application fields. A very long journey has been made from the first pioneering works, and this continuous search for more accurate solutions process has been further boosted in the last decade when deep neural networks have revolutionized the whole machine learning area, and gaze tracking as well. In this arena, it is being increasingly useful to find guidance through survey/review articles collecting most relevant works and putting clear pros and cons of existing techniques, also by introducing a precise taxonomy. This kind of manuscripts allows researchers and technicians to choose the better way to move towards their application or scientific goals. In the literature, there exist holistic and specifically technological survey documents (even if not updated), but, unfortunately, there is not an overview discussing how the great advancements in computer vision have impacted gaze tracking. Thus, this work represents an attempt to fill this gap, also introducing a wider point of view that brings to a new taxonomy (extending the consolidated ones) by considering gaze tracking as a more exhaustive task that aims at estimating gaze target from different perspectives: from the eye of the beholder (first-person view), from an external camera framing the beholder’s, from a third-person view looking at the scene where the beholder is placed in, and from an external view independent from the beholder

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

Engineering systematic musicology : methods and services for computational and empirical music research

One of the main research questions of *systematic musicology* is concerned with how people make sense of their musical environment. It is concerned with signification and meaning-formation and relates musical structures to effects of music. These fundamental aspects can be approached from many different directions. One could take a cultural perspective where music is considered a phenomenon of human expression, firmly embedded in tradition. Another approach would be a cognitive perspective, where music is considered as an acoustical signal of which perception involves categorizations linked to representations and learning. A performance perspective where music is the outcome of human interaction is also an equally valid view. To understand a phenomenon combining multiple perspectives often makes sense. The methods employed within each of these approaches turn questions into concrete musicological research projects. It is safe to say that today many of these methods draw upon digital data and tools. Some of those general methods are feature extraction from audio and movement signals, machine learning, classification and statistics. However, the problem is that, very often, the *empirical and computational methods require technical solutions* beyond the skills of researchers that typically have a humanities background. At that point, these researchers need access to specialized technical knowledge to advance their research. My PhD-work should be seen within the context of that tradition. In many respects I adopt a problem-solving attitude to problems that are posed by research in systematic musicology. This work *explores solutions that are relevant for systematic musicology*. It does this by engineering solutions for measurement problems in empirical research and developing research software which facilitates computational research. These solutions are placed in an engineering-humanities plane. The first axis of the plane contrasts *services* with *methods*. Methods *in* systematic musicology propose ways to generate new insights in music related phenomena or contribute to how research can be done. Services *for* systematic musicology, on the other hand, support or automate research tasks which allow to change the scope of research. A shift in scope allows researchers to cope with larger data sets which offers a broader view on the phenomenon. The second axis indicates how important Music Information Retrieval (MIR) techniques are in a solution. MIR-techniques are contrasted with various techniques to support empirical research. My research resulted in a total of thirteen solutions which are placed in this plane. The description of seven of these are bundled in this dissertation. Three fall into the methods category and four in the services category. For example Tarsos presents a method to compare performance practice with theoretical scales on a large scale. SyncSink is an example of a service

Driver Attention based on Deep Learning for a Smart Vehicle to Driver (V2D) Interaction

La atención del conductor es un tópico interesante dentro del mundo de los vehículos inteligentes para la consecución de tareas que van desde la monitorización del conductor hasta la conducción autónoma. Esta tesis aborda este tópico basándose en algoritmos de aprendizaje profundo para conseguir una interacción inteligente entre el vehículo y el conductor. La monitorización del conductor requiere una estimación precisa de su mirada en un entorno 3D para conocer el estado de su atención. En esta tesis se aborda este problema usando una única cámara, para que pueda ser utilizada en aplicaciones reales, sin un alto coste y sin molestar al conductor. La herramienta desarrollada ha sido evaluada en una base de datos pública (DADA2000), obteniendo unos resultados similares a los obtenidos mediante un seguidor de ojos caro que no puede ser usado en un vehículo real. Además, ha sido usada en una aplicación que evalúa la atención del conductor en la transición de modo autónomo a manual de forma simulada, proponiendo el uso de una métrica novedosa para conocer el estado de la situación del conductor en base a su atención sobre los diferentes objetos de la escena. Por otro lado, se ha propuesto un algoritmo de estimación de atención del conductor, utilizando las últimas técnicas de aprendizaje profundo como son las conditional Generative Adversarial Networks (cGANs) y el Multi-Head Self-Attention. Esto permite enfatizar ciertas zonas de la escena al igual que lo haría un humano. El modelo ha sido entrenado y validado en dos bases de datos públicas (BDD-A y DADA2000) superando a otras propuestas del estado del arte y consiguiendo unos tiempos de inferencia que permiten su uso en aplicaciones reales. Por último, se ha desarrollado un modelo que aprovecha nuestro algoritmo de atención del conductor para comprender una escena de tráfico obteniendo la decisión tomada por el vehículo y su explicación, en base a las imágenes tomadas por una cámara situada en la parte frontal del vehículo. Ha sido entrenado en una base de datos pública (BDD-OIA) proponiendo un modelo que entiende la secuencia temporal de los eventos usando un Transformer Encoder, consiguiendo superar a otras propuestas del estado del arte. Además de su validación en la base de datos, ha sido implementado en una aplicación que interacciona con el conductor aconsejando sobre las decisiones a tomar y sus explicaciones ante diferentes casos de uso en un entorno simulado. Esta tesis explora y demuestra los beneficios de la atención del conductor para el mundo de los vehículos inteligentes, logrando una interacción vehículo conductor a través de las últimas técnicas de aprendizaje profundo

Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports