SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure

Pedestrian Mobility Mining with Movement Patterns

In street-based mobility mining, pedestrian volume estimation receives increasing attention, as it provides important applications such as billboard evaluation, attraction ranking and emergency support systems. In practice, empirical measurements are sparse due to budget limitations and constrained mounting options. Therefore, estimation of pedestrian quantity is required to perform pedestrian mobility analysis at unobserved locations. Accurate pedestrian mobility analysis is difficult to achieve due to the non-random path selection of individual pedestrians (resulting from motivated movement behaviour), causing the pedestrian volumes to distribute non-uniformly among the traffic network. Existing approaches (pedestrian simulations and data mining methods) are hard to adjust to sensor measurements or require more expensive input data (e.g. high fidelity floor plans or total number of pedestrians in the site) and are thus unfeasible. In order to achieve a mobility model that encodes pedestrian volumes accurately, we propose two methods under the regression framework which overcome the limitations of existing methods. Namely, these two methods incorporate not just topological information and episodic sensor readings, but also prior knowledge on movement preferences and movement patterns. The first one is based on Least Squares Regression (LSR). The advantage of this method is the easy inclusion of route choice heuristics and robustness towards contradicting measurements. The second method is Gaussian Process Regression (GPR). The advantages of this method are the possibilities to include expert knowledge on pedestrian movement and to estimate the uncertainty in predicting the unknown frequencies. Furthermore the kernel matrix of the pedestrian frequencies returned by the method supports sensor placement decisions. Major benefits of the regression approach are (1) seamless integration of expert data and (2) simple reproduction of sensor measurements. Further advantages are (3) invariance of the results against traffic network homeomorphism and (4) the computational complexity depends not on the number of modeled pedestrians but on the traffic network complexity. We compare our novel approaches to state-of-the-art pedestrian simulation (Generalized Centrifugal Force Model) as well as existing Data Mining methods for traffic volume estimation (Spatial k-Nearest Neighbour) and commonly used graph kernels for the Gaussian Process Regression (Squared Exponential, Regularized Laplacian and Diffusion Kernel) in terms of prediction performance (measured with mean absolute error). Our methods showed significantly lower error rates. Since pattern knowledge is not easy to obtain, we present algorithms for pattern acquisition and analysis from Episodic Movement Data. The proposed analysis of Episodic Movement Data involve spatio-temporal aggregation of visits and flows, cluster analyses and dependency models. For pedestrian mobility data collection we further developed and successfully applied the recently evolved Bluetooth tracking technology. The introduced methods are combined to a system for pedestrian mobility analysis which comprises three layers. The Sensor Layer (1) monitors geo-coded sensor recordings on people’s presence and hands this episodic movement data in as input to the next layer. By use of standardized Open Geographic Consortium (OGC) compliant interfaces for data collection, we support seamless integration of various sensor technologies depending on the application requirements. The Query Layer (2) interacts with the user, who could ask for analyses within a given region and a certain time interval. Results are returned to the user in OGC conform Geography Markup Language (GML) format. The user query triggers the (3) Analysis Layer which utilizes the mobility model for pedestrian volume estimation. The proposed approach is promising for location performance evaluation and attractor identification. Thus, it was successfully applied to numerous industrial applications: Zurich central train station, the zoo of Duisburg (Germany) and a football stadium (Stade des Costières Nîmes, France)

Human experience in the natural and built environment : implications for research policy and practice

22nd IAPS conference. Edited book of abstracts. 427 pp. University of Strathclyde, Sheffield and West of Scotland Publication. ISBN: 978-0-94-764988-3

Physical Activity in Nature and Children\u27s Mental Health

The aim of this study was to determine if there is a relationship between children\u27s physical activity and wellbeing, and if that relationship is enhanced by physical activity in nature. This study was a non-experimental retrospective multi-informant data review conducted at an outpatient pediatric psychiatric clinic in the Northeast. The theoretical framework used to guide this study was the Health Promotion Model, suggesting Advanced Practice Nursing investigate the relationship between health promoting behaviors and personal factors that support mental wellness in children and protect against mental illness. Data collected included age, sex, and exercise and wellbeing subsections of the Vermont Child Health and Behavior Questionnaire (VHBQ): Parent Reports and Self-reports for 11-21 year olds. Data from three sample groups were analyzed: parent participants (n=178, 61% male, 38% female), child participants (n=78, 51% male, 49% female), and parent-child pairs with sex determined by child (n-25, 60% male, 40% female). Physical activity was calculated using a metric for participation in sports. Two sample t tests were used to analyze children\u27s response to the question do you participate in sports regularly? in relation to wellbeing scores. Pearson\u27s correlation coefficient was used to investigate correlations between 1) parent reports of their children\u27s physical activity and wellbeing, 2) children\u27s self-reports of physical activity and wellbeing, 3) parent reports of their children\u27s physical and children\u27s self-reports of physical activity, and 4) parent reports of their children\u27s wellbeing and children\u27s self-reports of wellbeing. Statistically significant results included positive correlations between parent reports of their children\u27s physical activity and wellbeing item, his/her living conditions are excellent (r=.34, p=\u3c0.001 for overall, r=.25, p=.002 for indoor, and r=.28, p=\u3c0.001 for outdoor). Positive weak correlations were found between parent reports of their children\u27s physical activity and scores on the VHBQ 10-point Worst Life/Best Life bar (r=.19, p=0.02 for overall and r=.17, p=.04 outdoor). Additionally, results showed significant strong positive correlations for all physical activities between parent reports of children\u27s participation and children\u27s self-report of participation (r=0.83, p=\u3c0.001 for overall, r=0.85, p=\u3c0.001 for indoor, and r=.67, p=0.02 for outdoor). However, only a single wellbeing item, Compared with...most peer, [child] is less happy than they are , demonstrated statistically significant positive correlation (r=.48, p=0.03) when parent reports and self-reports of wellbeing were compared. These results underscore the need for further research. Among professions, Advanced Practice Nurses may be best equipped to fully understand the lifestyle factors that promote children\u27s mental health. Moreover, because of their background, training and employment settings, Advanced Practice Nurses could play an important role not only in initiating well-being research studies, but also in using the resultant information to develop educational resources and policy

Enabling technologies for urban smart mobility: Recent trends, opportunities and challenges

The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encour-aging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility‐as‐a‐service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope

A Comparative Analysis of High-Speed Rail Station Development into Destination and Multi-Use Facilities: The Case of San Jose Diridon

As a burgeoning literature on high-speed rail development indicates, good station-area planning is a very important prerequisite for the eventual successful operation of a high-speed rail station; it can also trigger opportunities for economic development in the station area and the station-city. At the same time, “on the ground” experiences from international examples of high-speed rail stations can provide valuable lessons for the California high-speed rail system in general, and the San Jose Diridon station in particular. This study identifies and draws lessons from European HSR stations that share similarities across several criteria with the San Jose area context. From an initial consideration of twenty European HSR stations, the researchers chose five stations for in depth case studies: Euralille and Lyon Part Dieu in France, Rotterdam Centraal and Utrecht Centraal in the Netherlands, and Torino Porta Susa in Italy. Additionally, the study drew information from relevant local actors and stakeholders to better tailor recommendations to the particular California context.Through the undertaking of different research tasks–literature review, case studies of European railway stations, survey of existing station plans and other planning documents for the Diridon station, station area analysis, and interviews with station area planners and designers–the study compiles timely recommendations for the successful planning of the Diridon station and other stations along the California high-speed rail corridor

A Social Internet of Things Smart City Solution for Traffic and Pollution Monitoring in Cagliari

In the last years, the smart city paradigm has been deeply studied to support sustainable mobility and to improve human living conditions. In this context, a new smart city based on Social Internet of Things paradigm is presented in this article. Starting from the tracking of all vehicles (that is, private and public) and pedestrians, integrated with air quality measurements (that is, in real time by mobile and fixed sensors), the system aims to improve the viability of the city, both for pedestrian and vehicular users. A monitoring network based on sensors and devices hosted on board in local public transport allows real time monitoring of the most sensitive areas both from traffic congestion and from an environmental point of view. The proposed solution is equipped with an appropriate intelligence that takes into account instantaneous speed, type of traffic, and instantaneous pollution data, allowing to evaluate the congestion and pollution condition in a specific moment. Moreover, specific tools support the decisions of public administration facilitating the identification of the most appropriate actions for the implementation of effective policies relating to mobility. All collected data are elaborated in real time to improve traffic viability suggesting new directions and information to citizens to better organize how to live in the city

A Social Internet of Things Smart City Solution for Traffic and Pollution Monitoring in Cagliari

In the last years, the smart city (SC) paradigm has been deeply studied to support sustainable mobility and to improve human living conditions. In this context, a new SC based on the Social Internet of Things paradigm is presented in this article. Starting from the tracking of all vehicles (that is, private and public) and pedestrians, integrated with air quality measurements (that is, in real time by mobile and fixed sensors), the system aims to improve the viability of the city, both for pedestrian and vehicular users. A monitoring network based on sensors and devices hosted on board in local public transport allows real-time monitoring of the most sensitive areas both from traffic congestion and from an environmental point of view. The proposed solution is equipped with an appropriate intelligence that takes into account instantaneous speed, type of traffic, and instantaneous pollution data, allowing to evaluate the congestion and pollution condition in a specific moment. Moreover, specific tools support the decisions of public administration facilitating the identification of the most appropriate actions for the implementation of effective policies relating to mobility. All collected data are elaborated in real time to improve traffic viability suggesting new directions and information to citizens to better organize how to live in the city

Home is Where the Health Is: The Convergence of Environmental Justice, Affordable Housing, and Green Building

The issues this paper discusses–housing affordability, environmental equity, indoor and outdoor air quality, responsible use of natural resources, transportation and neighborhood character–are all connected. Green affordable housing is especially important in the context of the disproportionate effects that low-wealth households experience from environmental degradation, including air, water, and noise pollution. To frame this discussion, Part II of this paper discusses the concept of environmental justice, a relatively new topic in the arena of American environmental concerns. It looks at how the concept has evolved over time, to the point that it is no longer concerned only with disparate impacts of environmental hazards but also the equitable distribution of environmental benefits. Part III looks at what constitutes affordable housing, and how supplying it is a function of local governments’ land use authority. Part IV merges the concepts of affordable housing and environmental justice in the paradigm of green housing, demonstrating why both affordability and environmental justice are closely tied to issues of energy efficiency, transportation, indoor air quality, water conservation, and other attributes of green housing. Part V and VI conclude with observations about how law and policy can help establish comprehensive plans and legal mandates to insure that green features and affordability are incorporated in housing planning, as a matter of environmental justice