Guest Editorial: Sensor Informatics for Managing Mental Health

The papers in this special section focus on the topic of sensor informatics for mental health applications. The papers provide novel insights on advances in detection, sensing, analysis, and modeling of central and/or autonomic correlates useful in psychophysiological states assessment

Urban Emotions – Tools of Integrating People’s Perception into Urban Planning

This paper introduces the research field “Urban Emotions” – an interdisciplinary approach combining not only spatial planning and (geo-) informatics, but also computer linguistics and sensor technology methods. A new set of methods will be formed for the area of urban and spatial planning, resulting in a fundamental change of the understanding of planning. One of the main objectives is the involvement of citizens into planning processes. Therefore, new techniques are developed to collect and analyse data on the emotional perception of space and provide it to the people and also planners. Not only the human perception in the context of the city, and the combination with human sensory processes are contents of this paper, but also the critical discussion of these effects to privacy issues. Based on the topics “mental maps” and psychogeography in combination with the field of digital emotional urban tagging, the potential of integrating objectively quantified emotions in the context of citizen participation will be explained. In the following, partly established and partly experimental methods for collecting and analysing “Urban Emotions” will be introduced. Based on two studies, the possibilities of transfering these methodsinto the planning praxis will be shown on the one hand and on the other hand the potential for further development for other disciplines will be more evident

An Automated Text Mining Approach for Classifying Mental-Ill Health Incidents from Police Incident Logs for Data-Driven Intelligence

Data-driven intelligence can play a pivotal role in enhancing the effectiveness and efficiency of police service provision. Despite of police organizations being a rich source of qualitative data (present in less formally structured formats, such as the text logs), little work has been done in automating steps to allow this data to feed into intelligence-led policing tasks, such as demand analysis/prediction. This paper examines the use of police incident logs to better estimate the demand of officers across all incidents, with particular respect to the cases where mental-ill health played a primary part. Persons suffering from mental-ill health are significantly more likely to come into contact with the police, but statistics relating to how much actual police time is spent dealing with this type of incident are highly variable and often subjective. We present a novel deep learning based text mining approach, which allows accurate extraction of mental-ill health related incidents from police incident logs. The data gained from these automated analyses can enable both strategic and operational planning within police forces, allowing policy makers to develop long term strategies to tackle this issue, and to better plan for day-today demand on services. The proposed model has demonstrated the cross-validated classification accuracy of 89.5% on the real dataset

FullExpression - Emotion Recognition Software

During human evolution emotion expression became an important social tool that contributed to the complexification of societies. Human-computer interaction is commonly present in our daily life, and the industry is struggling for solutions that can analyze human emotions, in an attempt to provide better experiences. The purpose of this study was to understand if a software built using the transfer-learning technique on a deep learning model was capable of classifying human emotions, through facial expression analysis. A Convolutional Neuronal Network model was trained and used in a web application, which is available online. Several tools were created to facilitate the software development process, including the training and validation processes, and these are also available online. The data was collected after the combination of several facial expression emotion databases, such as KDEF_AKDEF, TFEID, Face_Place and jaffe. Software evaluation reveled an accuracy in identifying the correct emotions close to 80%. In addition, a comparison between the software and preliminary data from human’s performance, on recognizing facial expressed emotions, suggested that the software performed better. This work can be useful in many different domains such as marketing (to understand the effect of marketing campaigns on people’s emotional states), health (to help mental diseases diagnosis) and industry 4.0 (to create a better collaborating environment between humans and machines).Durante a evolução da espécie humana, a expressões de emoções tornou-se uma ferramenta social importante, que permitiu a criação de sociedades cada vez mais complexas. A interação entre humanos e máquinas acontece regularmente, evidenciando a necessidade da indústria desenvolver soluções que possam analisar emoções, de modo a proporcionar melhores experiências aos utilizadores. O propósito deste trabalho foi perceber se soluções de software desenvolvidas a partir da técnica de transfer-learning são capazes de classificar emoções humanas, a partir da análise de expressões faciais. Um modelo que implementa a arquitetura Convolutional Neuronal Network foi escolhido para ser treinado e utilizado na aplicação web desenvolvida neste trabalho, que está disponível online. A par da aplicação web, diferentes ferramentas foram criadas de forma a facilitar o processo de criação e avaliação de modelos Deep Learning, e estas também estão disponíveis online. Os dados foram recolhidos após a combinação de várias bases de dados de expressões de emoções (KDEF_AKDEF, TFEID, Face_Place and jaffe). A avaliação do software demostrou uma precisão na classificação de emoções próxima dos 80%. Para além disso, uma comparação entre o software e dados preliminares relativos ao reconhecimento de emoções por pessoas sugere que o software é melhor a classificar emoções. Os resultados deste trabalho podem aplicados em diversas áreas, como a publicidade (de forma a perceber os efeitos das campanhas no estado emocional das pessoas), a saúde (para um melhor diagnóstico de doenças mentais) e na indústria 4.0 (de forma a criar um melhor ambiente de colaboração entre humanos e máquinas)

Emotional facial processing in younger and older adults

There is evidence that older adults have difficulty processing negative but not positive facial expressions. This positivity effect among older adults is expressed in attention to as well as in memory and recognition of emotional faces. In the present thesis, effects of stimulus properties (i.e., self ratings of valence, arousal, potency), context, and visual exploration were investigated. In Study I, the aim was to investigate a happy face advantage seen in younger adults’ recognition and detection of facial expressions. Two recognition tasks showed that happy faces were better recognized than fearful and neutral faces. In addition, this superior effect was evident in early processing, indicating that happiness is an exceptional expression that is distinguished from other facial expressions. The objective of Study II was to investigate effects of age on subjective emotional impression (in terms of valence, arousal and potency) of angry and happy faces, and to examine whether any age differences were mirrored in measures of emotional behavior (attention, categorical perception, and memory). The results demonstrated that older adults perceived less arousal, potency, and valence than younger adults and that these differences were more pronounced for angry than happy faces. This was mirrored in larger age differences in attention, memory, and categorical perception for angry compared to happy faces. In Study III the aim was to investigate how linguistic context (i.e., written emotional labels) might reduce semantic confusability, and thereby facilitate facial expression recognition. The results showed that older adults were more reliant on linguistic context. Older and younger adults’ visual exploration patterns were investigated in Study IV. Results showed that older adults spent proportionally more time attending to the mouth than to the eyes, which might explain their relatively lower recognition of fear, anger and sadness, but maintained happiness and disgust recognition. In sum, subjective impression (i.e., arousal, potency), context, and visual exploration patterns interact with adult age and should be considered in research on effects of aging on facial expression processing

Sensor fusion in smart camera networks for ambient intelligence

This short report introduces the topics of PhD research that was conducted on 2008-2013 and was defended on July 2013. The PhD thesis covers sensor fusion theory, gathers it into a framework with design rules for fusion-friendly design of vision networks, and elaborates on the rules through fusion experiments performed with four distinct applications of Ambient Intelligence