The use of biometric monitoring allow researchers insight into the processing of environmental data by our central nervous systems. As a result we can determine precisely which stimuli cause arousal or draw our attention. This technology is used widely by commercial interests but is not commonly used to improve the public realm. Our authors hope to change this.
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The coronavirus pandemic highlighted the vital role urban areas play in supporting citizens’ health and well-being (Ribeiro et al., 2021). In times of (personal) vulnerability, citizens depend on their neighbourhood for performing daily physical activities to restore their mental state, but public spaces currently fall short in fulfilling the appropriate requirements to achieve this. The situation is exacerbated by Western ambitions to densify through high-rise developments to meet the housing demand. In this process of urban densification, public spaces are the carriers where global trends, local ambitions and the conditions for the social fabric materialise (Battisto & Wilhelm, 2020). High-rise developments in particular will determine users’ experiences at street-level. Consequently, they have an enduring influence on the liveability of neighbourhoods for the coming decades but, regarding the application of urban design principles, their impact is hard to dissect (Gifford, 2007).Promising emerging technologies and methods from the new transdisciplinary field of neuroarchitecture may help identify and monitor the impact of certain physical characteristics on human well-being in an evidence-based way. In the two-year Sensing Streetscapes research study, biometric tools were tested in triangulation with traditional methods of surveys and expert panels. The study unearthed situational evidence of the relationship between designed and perceived spaces by investigating the visual properties and experience of high-density environments in six major Western cities. Biometric technologies—Eye-Tracking, Galvanic Skin Response, mouse movement software and sound recording—were applied in a series of four laboratory tests (see Spanjar & Suurenbroek, 2020) and one outdoor test (see Hollander et al., 2021). The main aim was to measure the effects of applied design principles on users’ experiences, arousal levels and appreciation.Unintentionally, the research study implied the creation of a 360° built-environment assessment tool. The assessment tool enables researchers and planners to analyse (high-density) urban developments and, in particular, the architectural attributes that (subliminally) affect users’ experience, influencing their behaviour and perception of place. The tool opens new opportunities for research and planning practice to deconstruct the successes of existing high-density developments and apply the lessons learned for a more advanced, evidence-based promotion of human health and well-being.ReferencesBattisto, D., & Wilhelm, J. J. (Eds.). (2020). Architecture and Health Guiding Principles for Practice. Routledge, Taylor & Francis Group. Gifford, R. (2007). The Consequences of Living in High-Rise Buildings. Architectural Science Review, 50(1), 2–17. https://doi.org/https://doi.org/10.3763/asre.2007.5002 Hollander, J. B., Spanjar, G., Sussman, A., Suurenbroek, F., & Wang, M. (2021). Programming for the subliminal brain: biometric tools reveal architecture’s biological impact. In K. Menezes, P. de Oliveira-Smith, & A. V. Woodworth (Eds.), Programming for Health and Wellbeing in Architecture (pp. 136–149). Routledge, Taylor & Francis Group. https://doi.org/https://doi.org/10.4324/9781003164418 Ribeiro, A. I., Triguero-Mas, M., Jardim Santos, C., Gómez-Nieto, A., Cole, H., Anguelovski, I., Silva, F. M., & Baró, F. (2021). Exposure to nature and mental health outcomes during COVID-19 lockdown. A comparison between Portugal and Spain. Environment International, 154, 106664. https://doi.org/https://doi.org/10.1016/j.envint.2021.106664 Spanjar, G., & Suurenbroek, F. (2020). Eye-Tracking the City: Matching the Design of Streetscapes in High-Rise Environments with Users’ Visual Experiences. Journal of Digital Landscape Architecture (JoDLA), 5(2020), 374–385. https://gispoint.de/gisopen-paper/6344-eye-tracking-the-city-matching-the-design-of-streetscapes-in-high-rise-environments-with-users-visual-experiences.html?IDjournalTitle=6
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Background and objectivesBefore the Covid-19 pandemic, important social studies already indicated the severe negative feedback associated with high-rise developments. During the Covid-19 pandemic, citizens were confronted with their neighbourhoods’ insufficient restorative capacity to maintain their health and well-being. New methods are urgently required to analyse and learn from existing high-density developments to prevent a repetition of past mistakes and to catalyse the salutary effects of architecture in new developments.Process and methods (for empirical research)The Sensing Streetscapes research investigated the potential of emerging biometric technologies to examine the effects of commonly applied urban design principles in six western cities. In one outdoor and four laboratory tests, eye-tracking technology with sound-recording and Galvanic Skin Response captured subjects’ (un)conscious attention patterns and arousal levels when viewing streets on eye level. Triangulation with other techniques, such as mouse tracking to record participants’ appreciation value and expert panels from spatial design practice, showed the positive and negative impact of stimuli.Main results (or main arguments in the case of critical reviews)The preliminary results provide a dynamic understanding of urban experience and how it is affected by the presence or absence of design principles. The results suggest that streets with high levels of detail and variety may contribute to a high level of engagement with the built environment. It also shows that traffic is likely an important factor in causing stress and diminishing the restorative capacity society seeks.Implications for research and practice/policy | Importance and originality of the contributionThe research study led to the development of a Dynamic User Experience Assessment (D-UXA) tool that supports researchers and designers in understanding the impact of design decisions on users’ experience, spatial perception and (walking) behaviour. D-UXA enables a human-centred analysis and is designed to fill the gap between traditional empirical methods and aspirations for an evidence-based promotion of human health and wellbeing in (high-density) urban developments.
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African citizens are increasingly being surveilled, profiled, and targeted online in ways that violate their rights. African governments frequently use pandemic or terrorism-related security risks to grant themselves additional surveillance rights and significantly increase their collection of monitoring apparatus and technologies while spending billions of dollars to conduct surveillance (Roberts et al. 2023). Surveillance is a prominent strategy African governments use to limit civic space (Roberts and Mohamed Ali 2021). Digital technologies are not the root of surveillance in Africa because surveillance practices predate the digital age (Munoriyarwa and Mare 2023). Surveillance practices were first used by colonial governments, continued by post-colonial governments, and are currently being digitalized and accelerated by African countries. Throughout history, surveillance has been passed down from colonizers to liberators, and some African leaders have now automated it (Roberts et al. 2023). Many studies have been conducted on illegal state surveillance in the United States, China, and Europe (Feldstein 2019; Feldstein 2021). Less is known about the supply of surveillance technologies to Africa. With a population of almost 1.5 billion people, Africa is a continent where many citizens face surveillance with malicious intent. As mentioned in previous chapters, documenting the dimensions and drivers of digital surveillance in Africa is
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De African Digital Rights Network (ADRN) heeft een nieuw rapport gepubliceerd waarin de toevoer en verspreiding van digitale surveillance technologie in Afrika in kaart is gebracht. Onderzoeker Anand Sheombar van het lectoraat Procesinnovatie & Informatiesystemen is betrokken bij het ADRN-collectief en heeft samen met de Engelse journalist Sebastian Klovig Skelton, door middel van desk research de aanvoerlijnen vanuit Westerse en Noordelijke landen geanalyseerd. De bevindingen zijn te lezen in dit Supply-side report hoofdstuk van het rapport. APA-bronvermelding: Klovig Skelton, S., & Sheombar, A. (2023). Mapping the supply of surveillance technologies to Africa Supply-side report. In T. Roberts (Ed.), Mapping the Supply of Surveillance Technologies to Africa: Case Studies from Nigeria, Ghana, Morocco, Malawi, and Zambia (pp. 136-167). Brighton, UK: Institute of Development Studies.
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Digital surveillance technologies using artificial intelligence (AI) tools such as computer vision and facial recognition are becoming cheaper and easier to integrate into governance practices worldwide. Morocco serves as an example of how such technologies are becoming key tools of governance in authoritarian contexts. Based on qualitative fieldwork including semi-structured interviews, observation, and extensive desk reviews, this chapter focusses on the role played by AI-enhanced technology in urban surveillance and the control of migration between the Moroccan–Spanish borders. Two cross-cutting issues emerge: first, while international donors provide funding for urban and border surveillance projects, their role in enforcing transparency mechanisms in their implementation remains limited; second, Morocco’s existing legal framework hinders any kind of public oversight. Video surveillance is treated as the sole prerogative of the security apparatus, and so far public actors have avoided to engage directly with the topic. The lack of institutional oversight and public debate on the matter raise serious concerns on the extent to which the deployment of such technologies affects citizens’ rights. AI-enhanced surveillance is thus an intrinsically transnational challenge in which private interests of economic gain and public interests of national security collide with citizens’ human rights across the Global North/Global South divide.
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The security of online assessments is a major concern due to widespread cheating. One common form of cheating is impersonation, where students invite unauthorized persons to take assessments on their behalf. Several techniques exist to handle impersonation. Some researchers recommend use of integrity policy, but communicating the policy effectively to the students is a challenge. Others propose authentication methods like, password and fingerprint; they offer initial authentication but are vulnerable thereafter. Face recognition offers post-login authentication but necessitates additional hardware. Keystroke Dynamics (KD) has been used to provide post-login authentication without any additional hardware, but its use is limited to subjective assessment. In this work, we address impersonation in assessments with Multiple Choice Questions (MCQ). Our approach combines two key strategies: reinforcement of integrity policy for prevention, and keystroke-based random authentication for detection of impersonation. To the best of our knowledge, it is the first attempt to use keystroke dynamics for post-login authentication in the context of MCQ. We improve an online quiz tool for the data collection suited to our needs and use feature engineering to address the challenge of high-dimensional keystroke datasets. Using machine learning classifiers, we identify the best-performing model for authenticating the students. The results indicate that the highest accuracy (83%) is achieved by the Isolation Forest classifier. Furthermore, to validate the results, the approach is applied to Carnegie Mellon University (CMU) benchmark dataset, thereby achieving an improved accuracy of 94%. Though we also used mouse dynamics for authentication, but its subpar performance leads us to not consider it for our approach.
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In this project we take a look at the laws and regulations surrounding data collection using sensors in assistive technology and the literature on concerns of people about this technology. We also look into the Smart Teddy device and how it operates. An analysis required by the General Data Protection Regulation (GDPR) [5] will reveal the risks in terms of privacy and security in this project and how to mitigate them. https://nl.linkedin.com/in/haniers
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Among runners, there is a high drop-out rate due to injuries and loss of motivation. These runners often lack personalized guidance and support. While there is much potential for sports apps to act as (e-)coaches to help these runners to avoid injuries, set goals, and maintain good intentions, most available running apps primarily focus on persuasive design features like monitoring, they offer few or no features that support personalized guidance (e.g., personalized training schemes). Therefore, we give a detailed description of the working mechanism of Inspirun e-Coach app and on how this app uses a personalized coaching approach with automatic adaptation of training schemes based on biofeedback and GPS-data. We also share insights into how end-users experience this working mechanism. The primary conclusion of this study is that the working mechanism (if provided with accurate data) automatically adapts training sessions to the runners’ physical workload and stimulates runners’ goal perception, motivation, and experienced personalization. With this mechanism, we attempted to make optimal use of the potential of wearable technology to support the large group of novice or less experienced runners and that by providing insight in our working mechanisms, it can be applied in other technologies, wearables, and types of sports.
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Learning is all about feedback. Runners, for example, use apps like the RunKeeper. Research shows that apps like that enhance engagement and results. And people think it is fun. The essence being that the behavior of the runner is tracked and communicated back to the runner in a dashboard. We wondered if you can reach the same positive effect if you had a dashboard for Study-behaviour. For students. And what should you measure, track and communicate? We wondered if we could translate the Quantified Self Movement into a Quantified Student. So, together with students, professors and companies we started designing & building Quantified Student Apps. Apps that were measuring all kinds of study-behaviour related data. Things like Time On Campus, Time Online, Sleep, Exercise, Galvanic Skin Response, Study Results and so on. We developed tools to create study – information and prototyped the Apps with groups of student. At the same time we created a Big Data Lake and did a lot of Privacy research. The Big Difference between the Quantified Student Program and Learning Analytics is that we only present the data to the student. It is his/her data! It is his/her decision to act on it or not. The Quantified Student Apps are designed as a Big Mother never a Big Brother. The project has just started. But we already designed, created and learned a lot. 1. We designed and build for groups of prototypes for Study behavior Apps: a. Apps that measure sleep & exercise and compare it to study results, like MyRhytm; b. Apps that measure study hours and compare it to study results, like Nomi; c. Apps that measure group behavior and signal problems, like Groupmotion; d. Apps that measure on campus time and compare it with peers, like workhorse; 2. We researched student fysics to see if we could find his personal Cup-A-Soup-Moment (meaning, can we find by looking at his/her biometrics when the concentration levels dip?); 3. We created a Big Data lake with student data and Open Data and are looking for correlation and causality there. We already found some interesting patterns. In doing so we learned a lot. We learned it is often hard to acquire the right data. It is hard to create and App or a solution that is presenting the data in the right way and presents it in a form of actionable information. We learned that health trackers are still very inprecise. We learned about (and solved some) challenges surrounding privacy. Next year (2017) we will scale the most promising prototype, measure the effects, start a new researchproject and continu working on our data lake. Things will be interesting, and we will blog about it on www.quantifiedstudent.nl.
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