Assessing Essential Qualities of Urban Space with Emotional and Visual Data Based on GIS Technique

Finding a method to evaluate people’s emotional responses to urban spaces in a valid and objective way is fundamentally important for urban design practices and related policy making. Analysis of the essential qualities of urban space could be made both more effective and more accurate using innovative information techniques that have become available in the era of big data. This study introduces an integrated method based on geographical information systems (GIS) and an emotion-tracking technique to quantify the relationship between people’s emotional responses and urban space. This method can evaluate the degree to which people’s emotional responses are influenced by multiple urban characteristics such as building shapes and textures, isovist parameters, visual entropy, and visual fractals. The results indicate that urban spaces may influence people’s emotional responses through both spatial sequence arrangements and shifting scenario sequences. Emotional data were collected with body sensors and GPS devices. Spatial clustering was detected to target effective sampling locations; then, isovists were generated to extract building textures. Logistic regression and a receiver operating characteristic analysis were used to determine the key isovist parameters and the probabilities that they influenced people’s emotion. Finally, based on the results, we make some suggestions for design professionals in the field of urban space optimization

A Smart Home Network for Proactive Users

According to the European Strategy Energy Technology (SET) Plan, the resident-user engagement into thenational energy strategy is pivotal, as reported by the Challenge 1st: “Active consumer is at the centre of the energy system”. The Italian Ministry of Economic Development and ENEA have entered into a Program Agreement for the execution of the research and development lines of General Interest for the NationalElectricity System. In particular, as part of the “Development of an integrated model of the Urban Smart District” project. An experimental demonstration of a Smart Home network is being carried out in the Centocelle district of Rome and called “Smart Home Centocelle”. The project was developed in an informal settlement, which shares a common background with likewise urban settings, such as a lack of public transportation convenience or enjoyable public spaces and average quality housing, whereas people who adhered to the project have a medium-high education level and proved to be sensitive to alternative and more sustainable energy sources. Our research has examined the deployment progress made so far, gathering and analysing all the information to assess how the project applications could affect various quality-of-life dimensions: safety, health, environmental quality and personal comfort perception, social connectedness and the cost of living, above all

Capturing Primary School Students’ Emotional Responses with a Sensor Wristband

The emotions experienced by primary school students have both positive and negative effects on learning processes. Thus, to better understand learning processes, research should consider emotions during class. Standard survey-based methods, such as self-reports, are limited in terms of capturing the detailed trajectories of primary school  children’s emotions, as their abilities of self-reporting are developing and still limited. Emotions can also be tracked by capturing emotional responses as they occur e.g. from physiological reaction measured with sensor wristbands. This technology generates an emotional responsestypology based on continuously captured physiological data, such as skin conductivity and skin temperature. However, such measurement methods need to be validated before being used. The present study thus attempted to validate this instrument with primary school students. We used the BM Sensor Wristband technology, as its emotional response typology is based on the categorical emotion and homeostasis approach. In our research, we focus on the emotional responses that can be distinguished by the BM Typology and that can influence learning processes. These emotional responses are: “joy”, “curiosity”, “attention”, “fear”, “anger” and “passivity”. Therefore, we induced emotional responses in primary school children through specifically developed audio-visual stimuli. Using logistic mixed effects modelling, we investigated the occurrence of opposing reactions. We observed that primary school children’s reactions to audio-visual stimuli could be differentiated. We conclude that primary school children’s emotional responses, such as “joy”, “curiosity”, “attention”, “fear”, “anger” and “passivity”, can be accurately measured by evaluating physiological data

Orecchio: Extending Body-Language through Actuated Static and Dynamic Auricular Postures

In this paper, we propose using the auricle – the visible part of the ear – as a means of expressive output to extend body language to convey emotional states. With an initial exploratory study, we provide an initial set of dynamic and static auricular postures. Using these results, we examined the relationship between emotions and auricular postures, noting that dynamic postures involving stretching the top helix in fast (e.g., 2Hz) and slow speeds (1Hz) conveyed intense and mild pleasantness while static postures involving bending the side or top helix towards the center of the ear were associated with intense and mild unpleasantness. Based on the results, we developed a prototype (called Orrechio) with miniature motors, custommade robotic arms and other electronic components. A preliminary user evaluation showed that participants feel more comfortable using expressive auricular postures with people they are familiar with, and that it is a welcome addition to the vocabulary of human body language

The influence of urban visuospatial configuration on older adults’ stress: A wearable physiological-perceived stress sensing and data mining based-approach

Population ageing raises many fundamental questions, including how the urban environment can be configured to promote active ageing. The perceived element for older adults' involvement in the environment differs from the average person. Despite this difference, there is little to no research into understanding how the perceived elements (specifically, the visuospatial configuration) of the environment influence older adults' involvement—most studies focused on younger adults. The focus here is stress, which occurs when environmental demand exceeds a person's capability. As stress impacts a person's involvement in the environment and older adults are more likely to feel stress due to their decline in functional capability, it is important to understand how the visuospatial configuration of urban environment influence stress. Older adults were recruited to participate in an urban environment walk while their physiological responses (Photoplethysmogram) were monitored using wearable sensors. Their perceived stress responses were also collected. Spatial clustering and hot spot analysis were conducted to detect locations with clusters of physiological responses caused by spatial factors. These locations were subsequently labelled as stress or non-stress based on participants' perceived stress. The perceived visual elements of the urban environment were extracted using isovist analysis. Principal component analysis, self-organising map and machine learning algorithms were used to understand the relationship. The results demonstrate that isovist minimum visibility, occlusivity, and isovist area are the most influential determinants of older adults' physiological stress. Older adults prefer urban configurations where they can be seen. This study can be used to inform urban design and planning

Detecting stressful older adults-environment interactions to improve neighbourhood mobility: A multimodal physiological sensing, machine learning, and risk hotspot analysis-based approach

Not only is the global population ageing, but also the built environment infrastructure in many cities and communities are approaching their design life or showing significant deterioration. Such built environment conditions often become an environmental barrier that can either cause stress and/or limit the mobility of older adults in their neighbourhood. Current approaches to detecting stressful environmental interactions are less effective in terms of time, cost, labour, and individual stress detection. This study harnesses the recent advances in wearable sensing technologies, machine learning intelligence and hotspot analysis to develop and test a more efficient approach to detecting older adults' stressful interactions with the environment. Specifically, this study monitored older adults' physiological reactions (Photoplethysmogram and electrodermal activity) and global positioning system (GPS) trajectory using wearable sensors during an outdoor walk. Machine learning algorithms, including Gaussian Support Vector Machine, Ensemble bagged tree, and deep belief network were trained and tested to detect older adults' stressful interactions from their physiological signals, location and environmental data. The Ensemble bagged tree achieved the best performance (98.25% accuracy). The detected stressful interactions were geospatially referenced to the GPS data, and locations with high-risk clusters of stressful interactions were detected as risk stress hotspots for older adults. The detected risk stress hotspot locations corresponded to the places the older adults encountered environmental barriers, supported by site inspections, interviews and video records. The findings of this study will facilitate a near real-time assessment of the outdoor neighbourhood environment, hence improving the age-friendliness of cities and communities

Assessing Essential Qualities of Urban Space with Emotional and Visual Data Based on GIS Technique

Finding a method to evaluate people’s emotional responses to urban spaces in a valid and objective way is fundamentally important for urban design practices and related policy making. Analysis of the essential qualities of urban space could be made both more effective and more accurate using innovative information techniques that have become available in the era of big data. This study introduces an integrated method based on geographical information systems (GIS) and an emotion-tracking technique to quantify the relationship between people’s emotional responses and urban space. This method can evaluate the degree to which people’s emotional responses are influenced by multiple urban characteristics such as building shapes and textures, isovist parameters, visual entropy, and visual fractals. The results indicate that urban spaces may influence people’s emotional responses through both spatial sequence arrangements and shifting scenario sequences. Emotional data were collected with body sensors and GPS devices. Spatial clustering was detected to target effective sampling locations; then, isovists were generated to extract building textures. Logistic regression and a receiver operating characteristic analysis were used to determine the key isovist parameters and the probabilities that they influenced people’s emotion. Finally, based on the results, we make some suggestions for design professionals in the field of urban space optimization.ISSN:2220-996

Internet and Biometric Web Based Business Management Decision Support

Internet and Biometric Web Based Business Management Decision Support MICROBE MOOC material prepared under IO1/A5 Development of the MICROBE personalized MOOCs content and teaching materials Prepared by: A. Kaklauskas, A. Banaitis, I. Ubarte Vilnius Gediminas Technical University, Lithuania Project No: 2020-1-LT01-KA203-07810