Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns

We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures. A deep neural network classifies these textures into material types. This approach works effectively without the need for ambient light sources or direct contact with materials. Furthermore, the use of a deep learning network removes the need to handcraft the set of features for different materials. We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments. Our approach produced recognition accuracies above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584 images of 17 outdoor materials. We conclude by discussing its potentials for real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing System

Biosignal‐based human–machine interfaces for assistance and rehabilitation : a survey

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal‐based HMIs for assistance and rehabilitation to outline state‐of‐the‐art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full‐text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever‐growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complex-ity, so their usefulness should be carefully evaluated for the specific application

Detecting head movement using gyroscope data collected via in-ear wearables

Abstract. Head movement is considered as an effective, natural, and simple method to determine the pointing towards an object. Head movement detection technology has significant potentiality in diverse field of applications and studies in this field verify such claim. The application includes fields like users interaction with computers, controlling many devices externally, power wheelchair operation, detecting drivers’ drowsiness while they drive, video surveillance system, and many more. Due to the diversity in application, the method of detecting head movement is also wide-ranging. A number of approaches such as acoustic-based, video-based, computer-vision based, inertial sensor data based head movement detection methods have been introduced by researchers over the years. In order to generate inertial sensor data, various types of wearables are available for example wrist band, smart watch, head-mounted device, and so on. For this thesis, eSense — a representative earable device — that has built-in inertial sensor to generate gyroscope data is employed. This eSense device is a True Wireless Stereo (TWS) earbud. It is augmented with some key equipment such as a 6-axis inertial motion unit, a microphone, and dual mode Bluetooth (Bluetooth Classic and Bluetooth Low Energy). Features are extracted from gyroscope data collected via eSense device. Subsequently, four machine learning models — Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes, and Perceptron — are applied aiming to detect head movement. The performance of these models is evaluated by four different evaluation metrics such as Accuracy, Precision, Recall, and F1 score. Result shows that machine learning models that have been applied in this thesis are able to detect head movement. Comparing the performance of all these machine learning models, Random Forest performs better than others, it is able to detect head movement with approximately 77% accuracy. The accuracy rate of other three models such as Support Vector Machine, Naïve Bayes, and Perceptron is close to each other, where these models detect head movement with about 42%, 40%, and 39% accuracy, respectively. Besides, the result of other evaluation metrics like Precision, Recall, and F1 score verifies that using these machine learning models, different head direction such as left, right, or straight can be detected

Towards ai-based interactive game intervention to monitor concentration levels in children with attention deficit

—Preliminary results to a new approach for neurocognitive training on academic engagement and monitoring of attention levels in children with learning difficulties is presented. Machine Learning (ML) techniques and a Brain-Computer Interface (BCI) are used to develop an interactive AI-based game for educational therapy to monitor the progress of children’s concentration levels during specific cognitive tasks. Our approach resorts to data acquisition of brainwaves of children using electroencephalography (EEG) to classify concentration levels through model calibration. The real-time brainwave patterns are inputs to our game interface to monitor concentration levels. When the concentration drops, the educational game can personalize to the user by changing the challenge of the training or providing some new visual or auditory stimuli to the user in order to reduce the attention loss. To understand concentration level patterns, we collected brainwave data from children at various primary schools in Brazil who have intellectual disabilities e.g. autism spectrum disorder and attention deficit hyperactivity disorder. Preliminary results show that we successfully benchmarked (96%) the brainwave patterns acquired by using various classical ML techniques. The result obtained through the automatic classification of brainwaves will be fundamental to further develop our full approach. Positive feedback from questionnaires was obtained for both, the AI-based game and the engagement and motivation during the training sessions

Smart bus stop: people counting in a multi-view camera environment

As paragens de autocarros nos dias de hoje tem de estar cada vez mais ao serviço dos utentes, esta dissertação explora as ideias fundamentais sobre o que deve ser uma paragem de autocarro inteligente, reunindo num texto os conceitos mais utilizados e as mais recentes tecnologias sobre este tópico. Os fundamentos do que é uma paragem de autocarro inteligente são explorados, bem como a arquitetura de todo o sistema, não só a paragem propriamente dita. Ao analisar a bibliografia já existentes compreende-se que a paragem de autocarro não é uma entidade totalmente independente, pois esta está dependente de informação vinda de variadíssimas fontes. Assim sendo, a paragem de autocarro inteligente será um subsistema de um sistema muito mais complexo, composto pela própria paragem, pelo autocarro e por uma central. Em que a comunicação flui entre estes de forma a manter toda a informação do sistema atualizada em tempo real. O autocarro recolherá informação, como quantos passageiros tem abordo e a sua localização geográfica por exemplo. A central receberá toda a informação de todos os autocarros existentes assim como de todas as paragens de autocarro existentes. Por sua vez a paragem de autocarro, recolherá dados também, tais como quantas pessoas estão na paragem, temperatura, humidade, emissões de dióxido de carbono, ruido, entre outros. A paragem de autocarro deverá contar com um conjunto de interfaces de comunicação, tais como Bluetooth e/ou NFC, hi-fi e RFID ou Beacons, para que possam ser feitas comunicações com os utilizadores, com os autocarros e com a central. Deverá ter também ecrãs interativos que poderão ser acedidos usando gestos e/ou toque e/ou voz para que se possam efetuar as ações pretendidas. A informação não será apenas transmitida nos ecrãs interativos, será transmitida também através de som. A informação contida na paragem pode ser de todo o tipo, desde as rotas, horários, posição atual do próximo autocarro, assim como o número do mesmo, publicidade animada, etc. A paragem conta também com outras funcionalidades como conectores onde se possam carregar dispositivos móveis, aquecimento, iluminação controlada face à afluência de utilizadores e horário, um sistema de armazenamento de energia pois deverá contar com fontes de energia renováveis para que possa ser o mais autossustentável possível, e obviamente câmeras de vigilância para segurança dos utilizadores. Sendo o principal objetivo deste trabalho, o desenvolvimento de um algoritmo capaz de contar quantas pessoas se encontram na paragem de autocarro, através do processamento das imagens vindas de várias câmaras, o foco principal é explorar as tecnologias de visão computacional e como estas podem ser utilizadas dentro do conceito da paragem de autocarro inteligente. Uma vez que o mundo da visão computacional evoluiu muito nos últimos anos e as suas aplicações são quase ilimitadas, dai a sua implementação nas mais diversas áreas, como reconstrução de cenários, deteção de eventos, monitorização de vídeo, reconhecimento de objetos, estimativa de movimento, restauração de imagem, etc. Ao combinar os diferentes algoritmos das diferentes aplicações, podem ser criadas ferramentas mais poderosas. Assim sendo o algoritmo desenvolvido utiliza redes neuronais convulsionais para detetar todas as pessoas de uma imagem, devolvendo uma região de interesse. Essa região de interesse é processada em busca de caras e caso estas existam essa informação é guardada no perfil da pessoa. Isto é possível através da utilização de reconhecimento facial, que utiliza um algoritmo de Deep Learning (DL). Essa região de interesse também é convertida para uma escala de cinzentos e posteriormente para uma matriz, essa matriz será também guardada no perfil do utilizador. Está informação é necessária para que se possa treinar um modelo que utiliza algoritmos de aprendizagem de máquina (Support Vector Machine - SVM). Os algoritmos de DL e SVM são necessários para que se possa fazer a identificação dos utilizadores a cada imagem e para que se possa cruzar os vários perfis vindos das várias origens, para que possa eliminar os perfis repetidos. Com isto a mesma pessoa é contada as vezes que apareça nas imagens, em função do número de câmeras existentes na paragem. Assim sendo é preciso eliminar essas repetições de forma a ter um número de pessoas correto. Num ambiente controlado o algoritmo proposto tem uma taxa de sucesso elevada, praticamente sem falhas, mas quando testado no ambiente para o qual foi desenhado já não é bem assim, ou porque numa paragem de autocarro as pessoas estão em contante movimento ou porque ficam na frente umas das outras e não é possível visualizá-las a todas. Mesmo com muitas câmeras colocadas no local, acabam sempre por haver pontos mortos, devido à estrutura da paragem ou até mesmo devido ao meio, por exemplo árvores ou um carro mal-estacionado, etc.Bus stops nowadays have to be increasingly at the user’s service, this thesis explores the fundamentals ideas of what a Smart Bus Stop should be and bring all together into one concept using today’s technologies. Although the fundamentals of a Smart Bus Stop (SBS) are explored, the primary focus here is to explore computer vision technology and how they can be used inside the Smart Bus Stop concept. The world of computer vision has evolved a lot in recent years and its applications are almost limitless, so they have been incorporated into many different areas like scene reconstruction, event detection, video tracking, object recognition, motion estimation, image restoration, etc. When combining the different algorithms of the different applications more powerful tools can be created. This work uses a Convolutional Neural Network (CNN) based algorithm to detect people in a multi video feeds. It also counts the number of persons in the SBS, using facial recognition, using with Deep Learning algorithm, and Support Vector Machine algorithm. It is important to stress, these last two are used to keep track of the user and also to remove the repeated profiles gathered in the different video sources, since the SBS is in a multi-camera environment. Combining these technologies was possible to count how many people were in the SBS. In laboratory the propose algorithm presents an extremely high success rate, when applied to real bus stops que success rate decreases due to blind spots for instance

Multimodal Shared-Control Interaction for Mobile Robots in AAL Environments

This dissertation investigates the design, development and implementation of cognitively adequate, safe and robust, spatially-related, multimodal interaction between human operators and mobile robots in Ambient Assisted Living environments both from the theoretical and practical perspectives. By focusing on different aspects of the concept Interaction, the essential contribution of this dissertation is divided into three main research packages; namely, Formal Interaction, Spatial Interaction and Multimodal Interaction in AAL. As the principle package, in Formal Interaction, research effort is dedicated to developing a formal language based interaction modelling and management solution process and a unified dialogue modelling approach. This package aims to enable a robust, flexible, and context-sensitive, yet formally controllable and tractable interaction. This type of interaction can be used to support the interaction management of any complex interactive systems, including the ones covered in the other two research packages. In the second research package, Spatial Interaction, a general qualitative spatial knowledge based multi-level conceptual model is developed and proposed. The goal is to support a spatially-related interaction in human-robot collaborative navigation. With a model-based computational framework, the proposed conceptual model has been implemented and integrated into a practical interactive system which has been evaluated by empirical studies. It has been particularly tested with respect to a set of high-level and model-based conceptual strategies for resolving the frequent spatially-related communication problems in human-robot interaction. Last but not least, in Multimodal Interaction in AAL, attention is drawn to design, development and implementation of multimodal interaction for elderly persons. In this elderly-friendly scenario, ageing-related characteristics are carefully considered for an effective and efficient interaction. Moreover, a standard model based empirical framework for evaluating multimodal interaction is provided. This framework was especially applied to evaluate a minutely developed and systematically improved elderly-friendly multimodal interactive system through a series of empirical studies with groups of elderly persons

Systemic-functional linguistics and computation : New directions, new challenges

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