Estimating self-assessed personality from body movements and proximity in crowded mingling scenarios

ArtículoThis paper focuses on the automatic classi cation of self- assessed personality traits from the HEXACO inventory du- ring crowded mingle scenarios. We exploit acceleration and proximity data from a wearable device hung around the neck. Unlike most state-of-the-art studies, addressing per- sonality estimation during mingle scenarios provides a cha- llenging social context as people interact dynamically and freely in a face-to-face setting. While many former studies use audio to extract speech-related features, we present a novel method of extracting an individual's speaking status from a single body worn triaxial accelerometer which scales easily to large populations. Moreover, by fusing both speech and movement energy related cues from just acceleration, our experimental results show improvements on the estima- tion of Humility over features extracted from a single behav- ioral modality. We validated our method on 71 participants where we obtained an accuracy of 69% for Honesty, Consci- entiousness and Openness to Experience. To our knowledge, this is the largest validation of personality estimation carried out in such a social context with simple wearable sensors

ConfLab: A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions in the Wild

Recording the dynamics of unscripted human interactions in the wild is challenging due to the delicate trade-offs between several factors: participant privacy, ecological validity, data fidelity, and logistical overheads. To address these, following a 'datasets for the community by the community' ethos, we propose the Conference Living Lab (ConfLab): a new concept for multimodal multisensor data collection of in-the-wild free-standing social conversations. For the first instantiation of ConfLab described here, we organized a real-life professional networking event at a major international conference. Involving 48 conference attendees, the dataset captures a diverse mix of status, acquaintance, and networking motivations. Our capture setup improves upon the data fidelity of prior in-the-wild datasets while retaining privacy sensitivity: 8 videos (1920x1080, 60 fps) from a non-invasive overhead view, and custom wearable sensors with onboard recording of body motion (full 9-axis IMU), privacy-preserving low-frequency audio (1250 Hz), and Bluetooth-based proximity. Additionally, we developed custom solutions for distributed hardware synchronization at acquisition, and time-efficient continuous annotation of body keypoints and actions at high sampling rates. Our benchmarks showcase some of the open research tasks related to in-the-wild privacy-preserving social data analysis: keypoints detection from overhead camera views, skeleton-based no-audio speaker detection, and F-formation detection.Comment: v2 is the version submitted to Neurips 2022 Datasets and Benchmarks Trac

Devising and evaluating wearable technology for social dynamics monitoring

The importance of studying social interactions has been proven useful in several fields. In the workplace, studies have found that allowing mixing among different groups could improve team coordination and productivity. Architectural studies have analysed how physical spaces can potentially increase unplanned interactions. Other areas such as epidemiology have also benefited from tracking face-to-face contacts to study the spread of disease. Although technology has progressed significantly, the automated and accurate measurement of human interactions with mobile devices is still lagging. The main shortcomings have to do with accuracy of the captured data and with the communication modalities considered. Additionally, non-verbal behaviours during social interactions (e.g. body posture, orientation and interaction distance) have been often neglected, with a few exceptions, even if traditional sociology has highlighted their importance. In this dissertation we address these challenges by developing two wearable research platforms to monitor different dimensions of social interactions. First, we study the extent to which Bluetooth Low Energy could detect proximity in indoor environments. We analyse all the relevant protocol parameters and measure their impact on power consumption, on custom as well as on commercial devices. We assess its accuracy with a 4-week long deployment illustrating its sustainability for social dynamics studies. With the contacts and mobility data collected during the deployment we study the relationship between social contacts and space design, focusing on a modern architectural concept, Activity-Based Working (ABW). We uncover several patterns and we show how they could be the result of the correct adoption of ABW principles. However, we also discover that the employees might not have fully embraced the ABW concepts entirely, leading to mismatches between principles and actual space usage. Given the importance of studying non-verbal behaviour during social contact we then devise a novel wearable device that, by exploiting near-infrared signals, is able to capture accurate information about distance and angle of interaction between people. We show how we design the device to be robust to ambient light changes and short occlusions by leveraging inertial measurement units. With extensive testing we evaluate its accuracy and robustness. We then explore its potential to study creative processes by deploying it to capture non-verbal cues during a creative task. We show how data about the relative orientation between people and their interpersonal distance could be used to predict the role they have during the interaction and the status of the task. The platforms developed and the insights drawn in this dissertation provide evidence to support the use of wearable technologies to monitor social interactions at an unprecedented level

Estimating self-assessed personality from body movements and proximity in crowded mingling scenarios

\u3cp\u3eThis paper focuses on the automatic classification of self-Assessed personality traits from the HEXACO inventory du- ring crowded mingle scenarios. We exploit acceleration and proximity data from a wearable device hung around the neck. Unlike most state-of-The-Art studies, addressing personality estimation during mingle scenarios provides a challenging social context as people interact dynamically and freely in a face-To-face setting. While many former studies use audio to extract speech-related features, we present a novel method of extracting an individual's speaking status from a single body worn triaxial accelerometer which scales easily to large populations. Moreover, by fusing both speech and movement energy related cues from just acceleration, our experimental results show improvements on the estimation of Humility over features extracted from a single behavioral modality. We validated our method on 71 participants where we obtained an accuracy of 69% for Honesty, Conscientiousness and Openness to Experience. To our knowledge, this is the largest validation of personality estimation carried out in such a social context with simple wearable sensors.\u3c/p\u3

Urban Blue Spaces

This book presents an evidence-based approach to landscape planning and design for urban blue spaces that maximises the benefits to human health and well-being while minimising the risks. Based on applied research and evidence from primary and secondary data sources stemming from the EU-funded BlueHealth project, the book presents nature-based solutions to promote sustainable and resilient cities. Numerous cities around the world are located alongside bodies of water in the form of coastlines, lakes, rivers and canals, but the relationship between city inhabitants and these water sources has often been ambivalent. In many cities, water has been polluted, engineered or ignored completely. But, due to an increasing awareness of the strong connections between city, people, nature and water and health, this paradigm is shifting. The international editorial team, consisting of researchers and professionals across several disciplines, leads the reader through theoretical aspects, evidence, illustrated case studies, risk assessment and a series of validated tools to aid planning and design before finishing with overarching planning and design principles for a range of blue-space types. Over 200 full-colour illustrations accompany the case-study examples from geographic locations all over the world, including Portugal, the United Kingdom, China, Canada, the US, South Korea, Singapore, Norway and Estonia. With green and blue infrastructure now at the forefront of current policies and trends to promote healthy, sustainable cities, Urban Blue Spaces is a must-have for professionals and students in landscape planning, urban design and environmental design. Open Access for the book was funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 66677

Unmet goals of tracking: within-track heterogeneity of students' expectations for

Educational systems are often characterized by some form(s) of ability grouping, like tracking. Although substantial variation in the implementation of these practices exists, it is always the aim to improve teaching efficiency by creating homogeneous groups of students in terms of capabilities and performances as well as expected pathways. If students’ expected pathways (university, graduate school, or working) are in line with the goals of tracking, one might presume that these expectations are rather homogeneous within tracks and heterogeneous between tracks. In Flanders (the northern region of Belgium), the educational system consists of four tracks. Many students start out in the most prestigious, academic track. If they fail to gain the necessary credentials, they move to the less esteemed technical and vocational tracks. Therefore, the educational system has been called a 'cascade system'. We presume that this cascade system creates homogeneous expectations in the academic track, though heterogeneous expectations in the technical and vocational tracks. We use data from the International Study of City Youth (ISCY), gathered during the 2013-2014 school year from 2354 pupils of the tenth grade across 30 secondary schools in the city of Ghent, Flanders. Preliminary results suggest that the technical and vocational tracks show more heterogeneity in student’s expectations than the academic track. If tracking does not fulfill the desired goals in some tracks, tracking practices should be questioned as tracking occurs along social and ethnic lines, causing social inequality

Integrating host population contact structure and pathogen whole-genome sequence data to understand the epidemiology of infectious diseases : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy, Massey University, Manawatū, New Zealand

With advances in high-throughput sequencing technologies, computational biology, and evolutionary modelling, pathogen sequence data is increasingly being used to inform infectious disease outbreak investigations; supporting inferences on the timing and directionality of transmission as well as providing insights into pathogen evolutionary dynamics and the development of antimicrobial resistance. This thesis focuses on the application of pathogen whole-genome sequence data in conjunction with social network analysis to investigate the transmission dynamics of two important pathogens; Campylobacter jejuni and Staphylococcus aureus. The first four studies centre around the recent emergence of an antimicrobial-resistant C. jejuni strain that was found to have rapidly spread throughout the New Zealand commercial poultry industry. All four studies build on the results of an industry survey that were not only used to determine the basic farm demographics and biosecurity practices of all poultry producers, but also to construct five contact networks representing the on- and off-farm movement patterns of goods and services. Contact networks were used in study one to investigate the relationship between farm-level contact risk pathways and the reported level of biosecurity. However, despite many farms having a number of contact risk pathways, no relationship was found due to the high level of variability in biosecurity practices between producers. In study two the contact risk between commercial poultry, backyard poultry, and wild birds was investigated by examining the spatial overlap between the commercial contact networks and (i) all poultry transactions made through the online auction website TradeMe® and, (ii) all wild bird observations made through the online citizen science bird monitoring project, eBird, with study results suggesting that the greatest risk is due to the growing number of online trades made over increasingly long distances and shorter timespans. Study three further uses the commercial contact networks to investigate the role of multiple transmission pathways on the genetic relatedness of 167 C. jejuni isolates sampled from across 30 commercial poultry farms. Permutational multivariate analysis of variance and distance-based linear models were used to explore the relative importance of network distances as potential determinants of the pairwise genetic relatedness between the C. jejuni isolates, with study results highlighting the importance of transporting feed vehicles in addition to the geographical proximity of farms and the parent company in the spread of disease. In the last of the four C. jejuni studies, a compartmental disease transmission model was developed to simulate both the spread and sequence mutations across an outbreak within the commercial poultry industry. Simulated sequences were used in an analysis mirroring the methods used in study three in order to validate the approaches examining the contribution of local contacts and network contacts towards disease transmission. An additional analysis is also performed in which the simulated sequence data is used to infer a transmission tree and explore the use of pathogen phylogenies in determining who-infected-whom across different model systems. A further study, motivated by the application of whole-genome sequence data to infer transmission, investigated the spread of S. aureus within the New Zealand dairy industry. This study demonstrated how whole-genome sequence data can be used to investigate pathogen population and evolutionary dynamics at multiple scales: from local to national and international. For this study, the genetic relatedness between 57 bovine-derived S. aureus isolates sampled from across 17 New Zealand dairy herds were compared with 59 S. aureus isolates that had been previously sampled and characterised from humans and domestic pets from across New Zealand and 103 S. aureus isolates extracted from GenBank that included both human and livestock isolates sampled from across 19 countries. Results from this study not only support evidence showing that the movement of live animals is an important risk factor for the spread of S. aureus, but also show that using cattle-tracing data alone may not be enough to fully capture the between farm transmission dynamics of S. aureus. Overall, by using these two pathogen examples, this thesis demonstrates the potential use of pathogen whole-genome sequence data alongside contact network data in an epidemiological investigation, whilst highlighting the limitations and future challenges that must be considered in order to continue to develop robust methods that can be used to reliably infer the transmission and evolutionary dynamics across a range of infectious diseases