Image Processing for Pathological Visualization in Multitemporal Convoluted TIRI

The convoluted nature of thermal infrared radiation and poor understanding of the physical mechanismsof human emittance, make objective image acquisition and processing protocols prerequisite for meaningful diagnostic specificity. A longitudinal dataset of clinical thermal infrared images was objectively processed to facilitate visualization of osseous stress pathology in the lower limbs.. This paper details processing of 500+ thermal infrared images acquired during a recent three month clinical study into osseous stress pathology in the lower limbs of Australian Army basic trainees. The use ofthermal chroma-keying in segmentation and multitemporal image calibration is demonstrated. The ‘OpenSURF’ implementation of the scale and rotation-invariant interest point detector and escriptor are shown to be performant in registration of multitemporal clinical thermal infrared image data. Thermal ‘signs’ observed in longitudinal images appear to be revealing detectable changes in osseous stress pathophysiology

Emotions detection scheme using facial skin temperature and heart rate variability

Technology nowadays is aiming to provide a better life quality for people, schools and universities are working for the convenient of the students as well as ensuring a high quality of education is attained. Emotions detections system can be a solution for better education results and may also be used as a part of human-computer interaction applications such as robotics, games, and intelligent tutoring system, This study shows potentials method of detecting emotions using mobile computing to recognize and identify emotions (Relax, Fear, Sadness, and Joy) based on facial skin temperature, more specifically 5 spots on the face, Nose, Glabellar line (between the eyes and eyebrows) right\lift cheeks and the chin, in addition to the Heart Rate Variability (HRV). An experiment was conducted with 20 healthy subjects (10 females and 10 males, 20 to 31 years old), Both visual and auditory media were used to induce these emotions in the experiment. By the end of this paper, the output data will be anglicized by an Artificial neural network (ANN) The Multilayer Perceptron (MLP) was selected as a classifier with a result of 88.75 % accuracy. This mechanism proves that human`s emotions can easily identify without physical interaction with the subject and with high reliability with only 0.11 misprediction rat

Implementation of wavelet analysis on thermal images for affective states recognition of children with autism spectrum disorder

Children with Autism Spectrum Disorder are identified as a group of people who has difficulties in socio-emotional interaction. Most of them lack the proper context in producing social response through facial expression and speech. Since emotion is the key for effective social interaction, it is justifiably vital for them to comprehend the correct emotion expressions and recognitions. Emotion is a type of affective states and can be detected through physical reaction and physiological signals. In general, recognition of affective states from physical reaction such as facial expression and speech for autistic children is often unpredictable. Hence, an alternative method of identifying the affective states through physiological signals is proposed. Though considered non-invasive, most of the current recognition methods require sensors to be patched on to the skin body to measure the signals. This would most likely cause discomfort to the children and mask their 'true' affective states. The study proposed the use of thermal imaging modality as a passive medium to analyze the physiological signals associated with the affective states nonobtrusively. The study hypothesized that, the impact of cutaneous temperature changes due to the pulsating blood flow in the blood vessels at the frontal face area measured from the modality could have a direct impact to the different affective states of autistic children. A structured experimental setup was designed to measure thermal imaging data generated from different affective state expressions induced using different sets of audio-video stimuli. A wavelet-based technique for pattern detection in time series was deployed to spot the changes measured from the region of interest. In the study, the affective state model for typical developing children aged between 5 and 9 years old was used as the baseline to evaluate the performance of the affective state classifier for autistic children. The results from the classifier showed the efficacy of the technique and accorded good performance of classification accuracy at 88% in identifying the affective states of autistic children. The results were momentous in distinguishing basic affective states and the information could provide a more effective response towards improving social-emotion interaction amongst the autistic children

Facial Thermal and Blood Perfusion Patterns of Human Emotions: Proof-of-Concept

In this work, a preliminary study of proof-of-concept was conducted to evaluate the performance of the thermographic and blood perfusion data when emotions of positive and negative valence are applied, where the blood perfusion data are obtained from the thermographic data. The images were obtained for baseline, positive, and negative valence according to the protocol of the Geneva Affective Picture Database. Absolute and percentage differences of average values of the data between the valences and the baseline were calculated for different regions of interest (forehead, periorbital eyes, cheeks, nose and upper lips). For negative valence, a decrease in temperature and blood perfusion was observed in the regions of interest, and the effect was greater on the left side than on the right side. In positive valence, the temperature and blood perfusion increased in some cases, showing a complex pattern. The temperature and perfusion of the nose was reduced for both valences, which is indicative of the arousal dimension. The blood perfusion images were found to be greater contrast; the percentage differences in the blood perfusion images are greater than those obtained in thermographic images. Moreover, the blood perfusion images, and vasomotor answer are consistent, therefore, they can be a better biomarker than thermographic analysis in identifying emotions.Comment: 22 pages, 9 figure

Classifying pretended and evoked facial expressions of positive and negative affective states using infrared measurement of skin temperature

Earlier researchers were able to extract the transient facial thermal features from thermal infrared images (TIRIs) to make binary distinctions between the expressions of affective states. However, effective human-computer interaction would require machines to distinguish between the subtle facial expressions of affective states. This work, for the first time, attempts to use the transient facial thermal features for recognizing a much wider range of facial expressions. A database of 324 time-sequential, visible-spectrum, and thermal facial images was developed representing different facial expressions from 23 participants in different situations. A novel facial thermal feature extraction, selection, and classification approach was developed and invoked on various Gaussian mixture models constructed using: neutral and pretended happy and sad faces, faces with multiple positive and negative facial expressions, faces with neutral and six (pretended) basic facial expressions, and faces with evoked happiness, sadness, disgust, and anger. This work demonstrates that (1) infrared imaging can be used to observe the affective-state-specific facial thermal variations, (2) pixel-grey level analysis of TIRIs can help localise significant facial thermal feature points along the major facial muscles, and (3) cluster-analytic classification of transient thermal features can help distinguish between the facial expressions of affective states in an optimized eigenspace of input thermal feature vectors. The observed classification results exhibited influence of a Gaussian mixture model's structure on classifier-performance. The work also unveiled some pertinent aspects of future research on the use of facial thermal features in automated facial expression classification and affect recognition

Assessing equine emotional state

The scientific study of animal emotion has recently become an important focus for animal behaviour and welfare researchers. For horses used by humans for work, recreation or sport, the question of the significance of their life experiences in terms of their emotional response, is an important one if we are to provide for their welfare needs. Horses have received relatively less scientific attention than many livestock species when it comes to investigating emotional state or affective experience, although their behavioural responses during sporting or recreational performance are often described anecdotally using terminology indicating an underlying presumption of equine emotions. Indeed, the international governing body for equestrian sport, the Fédération Equestre Internationale (FEI), include the concept of 'the Happy Equine Athlete' into their rules, as a key objective during training and competition. This review presents available evidence to date of the physiological, behavioural and cognitive components of equine emotion and evaluates the extent to which the question concerning 'how horses feel' can be answered. The characterization of equine emotion in terms of level of arousal and valence, based on physiological, behavioural and cognitive indicators, offers a way forward to determine the impact of different situations and experiences on horses during their working lives. There is a need to develop robust validated methods for accessing equine emotions, to underpin a universally agreed method for/approach to providing an accurate assessment of equine welfare that can be utilized in a variety of contexts. This will provide a means of monitoring and improving the horse's experience, ensuring that the horse enjoys a good life, rather than one that is just worth living

Development Of a Multisensorial System For Emotions Recognition

Automated reading and analysis of human emotion has the potential to be a powerful tool to develop a wide variety of applications, such as human-computer interaction systems, but, at the same time, this is a very difficult issue because the human communication is very complex. Humans employ multiple sensory systems in emotion recognition. At the same way, an emotionally intelligent machine requires multiples sensors to be able to create an affective interaction with users. Thus, this Master thesis proposes the development of a multisensorial system for automatic emotion recognition. The multisensorial system is composed of three sensors, which allowed exploring different emotional aspects, as the eye tracking, using the IR-PCR technique, helped conducting studies about visual social attention; the Kinect, in conjunction with the FACS-AU system technique, allowed developing a tool for facial expression recognition; and the thermal camera, using the FT-RoI technique, was employed for detecting facial thermal variation. When performing the multisensorial integration of the system, it was possible to obtain a more complete and varied analysis of the emotional aspects, allowing evaluate focal attention, valence comprehension, valence expressions, facial expression, valence recognition and arousal recognition. Experiments were performed with sixteen healthy adult volunteers and 105 healthy children volunteers and the results were the developed system, which was able to detect eye gaze, recognize facial expression and estimate the valence and arousal for emotion recognition, This system also presents the potential to analyzed emotions of people by facial features using contactless sensors in semi-structured environments, such as clinics, laboratories, or classrooms. This system also presents the potential to become an embedded tool in robots to endow these machines with an emotional intelligence for a more natural interaction with humans. Keywords: emotion recognition, eye tracking, facial expression, facial thermal variation, integration multisensoria

UNOBTRUSIVE Technique Based On Infrared Thermal Imaging For Emotion Recognition In Children- With-asd- Robot Interaction

Emoções são relevantes para as relações sociais, e indivíduos com Transtorno do Espectro Autista (TEA) possuem compreensão e expressão de emoções prejudicadas. Esta tese consiste em estudos sobre a análise de emoções em crianças com desenvolvimento típico e crianças com TEA (idade entre 7 e 12 anos), por meio do imageamento térmico infravermelho (ITIV), uma técnica segura e não obtrusiva (isenta de contato), usada para registrar variações de temperatura em regiões de interesse (RIs) da face, tais como testa, nariz, bochechas, queixo e regiões periorbital e perinasal. Um robô social chamado N-MARIA (Novo-Robô Autônomo Móvel para Interação com Autistas) foi usado como estímulo emocional e mediador de tarefas sociais e pedagógicas. O primeiro estudo avaliou a variação térmica facial para cinco emoções (alegria, tristeza, medo, nojo e surpresa), desencadeadas por estímulos audiovisuais afetivos, em crianças com desenvolvimento típico. O segundo estudo avaliou a variação térmica facial para três emoções (alegria, surpresa e medo), desencadeadas pelo robô social N-MARIA, em crianças com desenvolvimento típico. No terceiro estudo, duas sessões foram realizadas com crianças com TEA, nas quais tarefas sociais e pedagógicas foram avaliadas tendo o robô N-MARIA como ferramenta e mediador da interação com as crianças. Uma análise emocional por variação térmica da face foi possível na segunda sessão, na qual o robô foi o estímulo para desencadear alegria, surpresa ou medo. Além disso, profissionais (professores, terapeuta ocupacional e psicóloga) avaliaram a usabilidade do robô social. Em geral, os resultados mostraram que o ITIV foi uma técnica eficiente para avaliar as emoções por meio de variações térmicas. No primeiro estudo, predominantes decréscimos térmicos foram observados na maioria das RIs, com as maiores variações de emissividade induzidas pelo nojo, felicidade e surpresa, e uma precisão maior que 85% para a classificação das cinco emoções. No segundo estudo, as maiores probabilidades de emoções detectadas pelo sistema de classificação foram para surpresa e alegria, e um aumento significativo de temperatura foi predominante no queixo e nariz. O terceiro estudo realizado com crianças com TEA encontrou aumentos térmicos significativos em todas as RIs e uma classificação com a maior probabilidade para surpresa. N-MARIA foi um estímulo promissor capaz de desencadear emoções positivas em crianças. A interação criança-com-TEA-e-robô foi positiva, com habilidades sociais e tarefas pedagógicas desempenhadas com sucesso pelas crianças. Além disso, a usabilidade do robô avaliada por profissionais alcançou pontuação satisfatória, indicando a N-MARIA como uma potencial ferramenta para terapias

Aspectos motivacionais no design de tecnologia para mudanças sociais

Orientador: Maria Cecília Calani BaranauskasTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Conectando pessoas e presente em todos os aspectos da vida, quando projetadas para este fim, as tecnologias têm potencial de influenciar a forma com que pessoas em um grupo social percebem e se relacionam com as coisas no ambiente. Este estudo de doutorado em Interação Humano-Computador (IHC) investiga como elementos motivacionais da Psicologia podem ser aplicados para informar o design, explo- rando esse potencial da tecnologia em promover mudanças sociais. O estudo é instanciado no domínio de consumo de energia elétrica, lidando com o desafio contemporâneo de cons- cientizar a sociedade dos limites naturais do planeta no que diz respeito ao uso de recursos naturais. Informar o design com aspectos motivacionais é uma abordagem recente em IHC. Quando encontrada na literatura, comumente tem foco em aspectos individuais e intrín- secos da motivação. Contudo, como argumentado nessa pesquisa, o contexto sociocultural evidencia a importância de considerar também os fatores externos que motivam as pessoas a se engajarem com uma tecnologia e com uma determinada questão social. Por considerar tanto fontes intrínsecas quanto extrínsecas de motivação, a Teoria da Autodeterminação é então considerada o principal referencial teórico da Psicologia nessa investigação, e a Semiótica Organizacional é a base metodológica para analisar os elemen- tos socioculturais que influenciam a motivação extrínseca. A análise situada dos dados socioculturais por uma perspectiva motivacional levou ao design da Tecnologia Socialmente Informada para Eco-Feedback de Energia (sigla SEET, em inglês), uma arquitetura que tem por objetivo estabelecer um novo padrão de com- portamento, ou uma nova maneira de perceber o consumo de energia coletivamente. O SEET é composto por um sistema interativo que promove colaboração, e pela Árvore da Energia, um dispositivo de feedback tangível para locais onde há encontro de pessoas. O SEET é avaliado em dois cenários complementares: uma Escola de Ensino Funda- mental no Brasil, onde os dados socioculturais foram coletados, analisados e aplicados para informar o design; e no contexto de um departamento de uma universidade no Reino Unido. Aspectos motivacionais da arquitetura do SEET são então analisadas, assim como o impacto dessa tecnologia ao desencadear as esperadas mudanças sociaisAbstract: By connecting people and being present in almost all aspects of life, when properly de- signed for that, technology can potentially influence the way people in a social group perceive and relate with things in their environment. This PhD study in the Human-Computer Interaction (HCI) field investigates how motivational elements from Psychology can be applied to inform the design aiming at exploring this potential of technology for promoting a social change. The study is in- stantiated in the energy consumption domain, coping with the contemporary challenge of raising awareness among the society of the planet¿s natural resources usage and limits. Informing the design with motivational aspects is a recent approach in HCI. When found in literature, it is mostly focused on individual and intrinsic aspects of motivation. However, as argued in this research, the sociocultural context evidences the importance of considering also the external factors that motivate people to be engaged with technology and the social issue. By taking into account both intrinsic and extrinsic sources of motivation, the Self- Determination Theory is then considered the main theoretical background from Psychol- ogy in this investigation, and the Organisational Semiotics the methodological basis to analyse sociocultural elements that influence extrinsic motivation. The situated analysis of sociocultural data with motivational lenses has led to the de- sign of a Socially-informed Energy Eco-feedback Technology (SEET), an architecture that aims at establishing a "new pattern of behaviour", or a new way of perceiving collective energy consumption. The SEET is composed by an interactive system that promotes collaboration and The Energy Tree, a tangible and public feedback device for gathering places. The SEET is evaluated in two complementary scenarios: an elementary school in Brazil, where the sociocultural data was collected, analysed and applied to inform design; and in the context of an university department in the United Kingdom. Motivational as- pects of the SEET architecture are then analysed, as well as the impact of this technology to trigger the desired social changeDoutoradoCiência da ComputaçãoDoutora em Ciência da Computaçã