Everyday Environments Research Articles

Older Adults' Needs and Wishes for Contact With the Outdoors at Residential Care Facilities: Implications for Theory and Practice.

Aim: This qualitative study aimed to explore needs and wishes of older adults concerning their perceived need for contact with outdoor environments at residential care facilities (RCFs) and what implications it has for theory and practice. Background: There is increased awareness of the importance of health-promoting everyday environments for persons with special needs. Therefore, it is important to include the experiences of older adults at RCFs in research. Methods: Twelve older adults from three Swedish RCFs participated in semistructured walking interviews. Results: Two categories were identified concerning the needs and wishes of older adults for contact with outdoor environments. The first category, Outdoor environments as part of everyday life, describes aspects of normality linked to outdoor stays at RCFs. The second category, Getting outdoors in practice, describes supportive and hindering aspects of outdoor stays, as well as accessibility regarding different body positions and access to personal support. Conclusion: It was found in this study that the needs and wishes of older adults are important to consider to increase their opportunities for outdoor stays. Their needs and wishes could also be included in briefs and programs for the design and planning of new construction or refurbishment of RCFs. The results of the study can serve as the basis for further discussions concerning older adults' outdoor stays and the accessibility of outdoor environments. Further, the results are intended to facilitate practical knowledge that is useful for care workers and managers at RCFs and to support decision makers, property developers, architects, and planners.

Development of electromagnetic pollution maps utilizing Gaussian process spatial models

The rapid proliferation of wireless technologies in everyday environments demands the quick and precise estimation of electromagnetic field distribution. This distribution is commonly depicted through the electric field strength across various geographical areas. The objective of this research is to determine the most effective geospatial model for generating a national-level electric field strength map within the 30 MHz–6 GHz frequency range. To achieve this, we employed five different methodologies for constructing the electric field strength map. Four of these methodologies are based on Gaussian process regression, while the fifth utilizes the classical weighted-average method of the nearest neighbor. Our study focused on a country with a total area of 9251 km2, using a dataset comprising 3621 measurements. The findings reveal that Gaussian process spatial models, also known as Kriging models, generally outperform other methods when applied to spatial data. However, it was observed that, after excluding some outlier data points, the performance of the classical nearest neighbor models becomes comparable to that of the Gaussian process models. This indicates the potential for both approaches to be effective, depending on the data quality and the presence of outliers.

Editorial Preview

Effective from Volume 3, JIWE has transitioned to a triannual publication release effort. Specifically, releases occur each February, June, and October. This particular October 2024 release contains 12 papers within the regular section that covers a broad range of application concerning Machine Learning (ML), Artificial Intelligence (AI), Data Mining (DM), Software Engineering, Recommender Systems, Cybersecurity, Healthcare, and other key areas in web engineering. Additionally, this edition presents a captivating collection of 7 papers curated by our Thematic Editor, Dr. Ji-Jian Chin, under the theme "Pervasive Computing." In his editorial, Dr. Ji-Jian Chin highlights cutting-edge research on the integration of computing into everyday environments. Moreover, these papers are aligned to some of the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) through advancements in healthcare technologies, SDG 9 (Industry, Innovation, and Infrastructure) through software and systems innovation research, and SDG 16 (Peace, Justice, and Strong Institutions) through contributions to privacy and cybersecurity research.

Heavy Metal Analysis Using Inductively Couple Plasma – Optical Emission Spectrometry (ICP-OES) on Hair of Breast Cancer Patients

Heavy metals can be found in everyday environments and can enter the human body through food, water, and air. Heavy metals are categorized into three groups by the World Health Organization (WHO): essential elements such as Copper (Cu), Chromium (Cr), Cobalt (Co), Iron (Fe), and Zinc (Zn); potentially essential elements like Manganese (Mn) and Nickel (Ni); and potentially toxic elements such as Cadmium (Cd) and Lead. (Pb). The heavy metal is a systemic poison that causes organ dysfunction in humans. The aim of this research is to analyze the levels of heavy metal elements Copper (Cu), Iron (Fe), and Zinc (Zn) in the hair of breast cancer patients. The method used to measure heavy metal content is Inductively Coupled Plasma – Optical Emission Spectrometry. (ICP-OES). The levels of heavy metal elements Cu, Fe, and Zn in five hair samples from breast cancer patients differ from the standards GBW07601 and IAEA-086. The levels of Cu and Fe in breast cancer patients are lower than the standard, while the level of Zn is higher than the standard.

The utility of high-dosage experiments in everyday life to test theories in clinical science.

This viewpoint article discusses the utility of high-dosage experiments (HDEs) in everyday life to test theories in clinical science. HDEs involve experimental manipulations and assessments that occur over much longer periods of time than traditional experiments-generally days or even weeks. By nature, they also occur outside the lab, in the everyday environments of participants. Additionally, as with other experiments, the purpose of the study is concealed from participants. Experimental design is one of the most distinguishable characteristics of psychology that separates it from other behavioral sciences. Studies that rely on experiments are essential for theory testing and establishing the potential causal role of mechanisms that underlie psychopathology. Yet despite the value of experimental research, experimental studies are not currently given special prominence in clinical psychological science. For example, in the Journal of Psychopathology and Clinical Science, of all the empirical studies in the most recent year (2023), only three of 77 incorporated an experimental manipulation. Experimental research appears to be less popular in clinical psychology than in other fields, such as social psychology. What might account for this discrepancy? First, clinical samples are more difficult to recruit. This is important because experimental manipulations may produce small effects that require large samples for detection. Additionally, mechanisms hypothesized to underlie psychopathology are often chronic and intransigent. For example, cognitive factors (e.g., perfectionistic beliefs) could require an especially strong manipulation to modify in isolation. Researchers have argued that psychology has been experiencing a crisis in theory development. Eronen and Bringmann (2021) stated that one major reason for this crisis is the difficulty in establishing causal relationships between psychological constructs. The replication crisis has garnered even more attention (Open Science Collaboration, 2015). HDEs would help address these two crises and provide stronger and more replicable tests of theory. This could allow us to more precisely identify important mechanisms underlying psychopathology, potentially enhancing treatment efficacy, and enabling us to move the field forward. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Synthesis and evaluation of bio-based Poly(oxime-urethane) with intrinsic antimicrobial properties

Intrinsic antimicrobial properties can give polyurethanes a wider range of applications. In this work, the bio-based chain extender, 2,5-diformylfuran dioxime (DFFD) possessing antimicrobial characteristic, is used to synthesize the intrinsically antimicrobial poly(oxime-urethane) (Dx-PU) with isophorone diisocyanate (IPDI) and poly(tetramethylene glycol) (PTMG-1000), and its properties are investigated. The results show that the tensile strength is 19.12 ± 0.49 MPa, the Young’s modulus of the poly(oxime-urethane) is 118.70 ± 11.40 MPa. The elongation at break and Shore A hardness were 292 ± 11 % and 93 ± 2, respectively. Additionally, the Dx-PU demonstrated a high thermal decomposition temperature of over 250 °C and a low glass transition temperature of the soft segments at approximately −60 °C. Meanwhile, research on Dx-PU coating has demonstrated that Dx-PU coating is 100 % antimicrobial against E. coli and S. aureus, and is suitable for use on a wide range of substrates, including glass, engineering plastics, metal and leather with excellent adhesion (ISO 0), pencil hardness (2H), impact resistance (0.5 m·kg) and flexibility (2 mm). The Dx-PU coating also has significant antibacterial performance in everyday environments. The outstanding flexibility, moderate Young’s modulus, high elongation at break, good thermal resistance, and excellent antimicrobial performance of Dx-PU make it highly suitable for applications on high-contact surfaces such as hospital walls, public sanitation facilities, countertops, and door handles. As a bio-based chain extender from renewable sources, DFFD offers new solutions for the synthesis of bio-based antimicrobial polyurethane and its coatings.

Do You “Relay” Want to Give Me Away? – Forensic Cues of Smart Relays and Their IoT Companion Apps

As IoT devices become more prevalent in everyday environments, their relevance to digital investigations increases. The product class of “smart relays”, which are connected to the low-voltage grid and usually installed in sockets behind walls, has not yet received much attention in the context of smart home forensics. To close a category-specific gap in the device forensics literature, we conducted a multi-device analysis of 16 smart relays from 9 manufacturers, which support six different companion apps in total. Our examination shows that forensic artifacts can be found locally on the smart relays and in the companion app data, as well as remotely on cloud servers of the vendors. Based on our findings, we developed a Python framework to extract forensic artifacts automatically from obtained firmware dumps, from companion app data, and from captured network traffic.

Humans Learn Language from Situated Communicative Interactions. What about Machines?

Abstract Humans acquire their native languages by taking part in communicative interactions with their caregivers. These interactions are meaningful, intentional, and situated in their everyday environment. The situated and communicative nature of the interactions is essential to the language acquisition process, as language learners depend on clues provided by the communicative environment to make sense of the utterances they perceive. As such, the linguistic knowledge they build up is rooted in linguistic forms, their meaning, and their communicative function. When it comes to machines, the situated, communicative, and interactional aspects of language learning are often passed over. This applies in particular to today’s large language models (LLMs), where the input is predominantly text-based, and where the distribution of character groups or words serves as a basis for modeling the meaning of linguistic expressions. In this article, we argue that this design choice lies at the root of a number of important limitations, in particular regarding the data hungriness of the models, their limited ability to perform human-like logical and pragmatic reasoning, and their susceptibility to biases. At the same time, we make a case for an alternative approach that models how artificial agents can acquire linguistic structures by participating in situated communicative interactions. Through a selection of experiments, we show how the linguistic knowledge that is captured in the resulting models is of a fundamentally different nature than the knowledge captured by LLMs and argue that this change of perspective provides a promising path towards more human-like language processing in machines.

Hydrogel‐Based Droplet Electricity Generators: Intrinsically Stretchable and Transparent for Seamless Integration in Diverse Environments

AbstractDroplet‐based electricity generators (DEGs) have emerged as innovative devices capable of transducing the kinetic energy of falling water droplets into electrical power, thereby presenting a promising solution for ambient energy harvesting. For effective application, DEGs must exhibit transparency and flexibility to facilitate aesthetic integration and optical/mechanical adaptability for utilization on natural objects and wearable devices. In this study, a hydrogel‐based DEG (Hy‐DEG) designed is proposed to be seamlessly integrated into everyday environments without compromising design or functionality. With the intrinsic characteristics of hydrogel, the Hy‐DEG exhibits superior optical transmittance (≈99%) and stretchability (≈70% of its original length), generating the electrical output performance of ≈45 V of voltage, 15 µA of current, and 42 µW of maximum peak power, with single droplet impact. This capability of the Hy‐DEG can operate electronic devices for environmental monitoring while maintaining seamless integration with commonly encountered surfaces. In this context, this study elucidates the characteristics of hydrogel bestowing novel functionalities upon the DEG, expanding the potential for energy harvesting in residential and commercial contexts. The implications of this research extend beyond conventional energy generation, fostering the development of self‐powered, transparent, and flexible devices.

A real-world evaluation of the clinical benefits of improved sound processor technology among Chinese cochlear implant users: A focus on Cochlear Nucleus 7.

Real-world evidence is increasingly used to support clinical and regulatory decisions globally and may be a useful tool to study the unique needs of cochlear implant users in China. The ability to recognize and understand speech in noise is critical for cochlear implant users, however, this remains a challenge in everyday settings with fluctuating competing noise levels. The Cochlear™ Sound Processor, Nucleus® 7 (CP1000), includes Forward Focus, a spatial noise algorithm aimed to improve speech-in-noise performance, and Made for iPhone/iPod/iPad functionality. We conducted a prospective, single-center, open-label, within-participant, real-world evidence investigation in participants with cochlear implants. The primary objective of this study, conducted in China, was to compare speech perception in spatially separated dynamic noise with the Nucleus 7 to the recipients' current older Cochlear Sound Processor, including the Freedom and Nucleus 5 sound processors. A follow-up study monitored participants from the initial study up to 12-months post the fitting of their Nucleus 7 and investigated hearing ability, satisfaction, and usability of the device via a questionnaire. Forty participants were included in the initial study (age-range 3 to 49 years) and 29 continued to the follow-up study (age-range 5 to 28 years). The participants were heterogeneous in terms of age, cochlear implant experience, and duration of hearing loss. Nucleus 7 significantly improved participant speech recognition performance in noise by 7.54 dB when compared with the participants' current older sound processor (p<0.0001). Overall satisfaction with Nucleus 7 was 72%. Satisfaction in different hearing contexts ranged from 93.1% for understanding a 1:1 conversation in a quiet setting, 62.1% for understanding on the phone, to 34.5% hearing in complex noisy situations. The study demonstrated the benefits of the Nucleus 7 sound processor across different hearing environments in a Chinese population and showed improved hearing ability, usability, and satisfaction in a real-world every-day environment.

Epsilon-near-zero thin films in a dual-functional system for thermal infrared camouflage and thermal management within the atmospheric window.

Thermal infrared camouflage aims to reduce the detectability of a target using thermal imaging devices. Given the typically high thermal emissivity in everyday environments, the thermal emissivity of the background environment must be considered. The conventional low-emissivity strategy for thermal camouflage is only effective for targets at extremely high temperatures (>350 °C), making it unsuitable for applications near room-to-medium-high temperature range (<350 °C). In this study, we introduce metallic glass into infrared thermal camouflage technology, exploiting its adjustable emissivity to accommodate diverse infrared thermal camouflage scenarios. Moreover, we combined metallic glass with the Berreman mode of epsilon-near-zero (ENZ) thin films (SiO2, Al2O3, and TiO2) for the first time. In the long wave infrared (LWIR, 8-14 μm) regions, the small viewing angle exhibits the optical properties of metallic glasses, while the large viewing angle (above 45°) provides high thermal emissivity in transverse-magnetic (TM) polarization. A thermal management function was provided without affecting the thermal camouflage performance. The cooling power exhibited by ENZ thin films on metallic glass surpassed that of the conventional low-emissivity strategy for thermal camouflage by a factor of 1.79. Furthermore, the thermal images indicated over 97% similarity in thermal radiation between the target and background environments. We developed a dual-function system for infrared camouflage and thermal management within an identical wavelength region of the atmospheric window.

Penilaian Kepuasaan User dalam Menggunakan APlikasi SIM ASN Menggunakan Metode Website Quality

Implementation of the State Civil Apparatus Management Information System (SIM ASN) is a must. The ASN SIM is not only an administrative tool, but is a digital ecosystem that integrates all personnel application services. The problem that occurs in the ASN SIM application is that users find it difficult to complete the complete history requested by the ASN SIM in the personnel database to update employee data history. The aim of this study is to assess the impact of three factors on user satisfaction: application quality, user-friendliness, and interaction quality. The evaluation process is based on the Website Quality technique, which takes into account multiple aspects of website quality, such as as information content, security, usability, speed, and interface design. Respondents in this study were frequent users of the ASN SIM Application. Questionnaires were used to gather information about user opinions and experiences with the quality of the program. To characterize the degree of user satisfaction and the variables influencing it, data analysis was carried out using both statistical and qualitative methodologies. In order to enhance the user experience and efficacy of using the ASN SIM Application in the everyday work environment, it is intended that the research findings will offer comprehensive insight into the areas that require further development. This study adds to the body of knowledge about user satisfaction evaluation in the context of government apps and may serve as a model for future research on the subject.

Deep learning approach for dysphagia detection by syllable-based speech analysis with daily conversations

Dysphagia, a disorder affecting the ability to swallow, has a high prevalence among the older adults and can lead to serious health complications. Therefore, early detection of dysphagia is important. This study evaluated the effectiveness of a newly developed deep learning model that analyzes syllable-segmented data for diagnosing dysphagia, an aspect not addressed in prior studies. The audio data of daily conversations were collected from 16 patients with dysphagia and 24 controls. The presence of dysphagia was determined by videofluoroscopic swallowing study. The data were segmented into syllables using a speech-to-text model and analyzed with a convolutional neural network to perform binary classification between the dysphagia patients and control group. The proposed model in this study was assessed in two different aspects. Firstly, with syllable-segmented analysis, it demonstrated a diagnostic accuracy of 0.794 for dysphagia, a sensitivity of 0.901, a specificity of 0.687, a positive predictive value of 0.742, and a negative predictive value of 0.874. Secondly, at the individual level, it achieved an overall accuracy of 0.900 and area under the curve of 0.953. This research highlights the potential of deep learning modal as an early, non-invasive, and simple method for detecting dysphagia in everyday environments.

The things they are a-changin': reinventing daily objects during the chilean 2019 revolt

In October 2019, fuelled by a collective discontent with the lifestyles and inequalities generated by the capitalist model, social protests took place in the Chilean streets. People staged massive demonstrations in every city demanding profound changes to the system. Known as the ‘Estallido Social’ (Social Explosion), the demonstrations paralysed an important part of the country and, after a few weeks of revolt, managed to initiate a new Constitution-making process. These events triggered a process in which the country was re-imagined, subjectivities were rewritten and objects and the environment were heavily transformed. This study places its focus on a set of industrially produced objects, whose original functions were subverted in the streets to enable new uses and meanings aligned with the Social Explosion. Kitchen items such as pots and ladles were used to make a lot of noise; road signs and pieces of urban furniture were re-located to block the streets; and gas cylinders and oil drums were reshaped for protection against police forces. While specialized literature generally seeks to recognize the new social forms and models that emerge from the crisis, this essay aims to reflect on the ways in which the relationship with the everyday environment is transformed in the midst of a social crisis, and how operations of resignification and reinterpretation may also be necessary to bring about more profound changes. As such, this study aims to characterize these operations of mutation (of bending, crumpling, scratching, wetting, moving, cutting, twisting, hitting, breaking, cracking, tricking, whipping, throwing, crushing, dragging, drilling and burning), and from there to move towards elucidating the practical and symbolic repercussions that this new material reality has had on the social body and daily life.

Recognizing the Role of ChatGPT in Decision-Making and Recognition of Mental Health Disorders among Entrepreneurs

Nowadays, mental health disorders have become a significant concern for everyone. There are diverse reasons emanating from the workplace, business, and everyday environment. Therefore, there is a current need to use technology to detect and review their symptoms and causes. Accordingly, in this study, the researcher attempted to recognize ChatGPT’s role in decision-making and the recognition of mental health disorders among Egyptian entrepreneurs. The researcher used a quantitative approach in this study based its findings on 332 valid samples. The study's results through path analysis using Analysis of Moment Structures (AMOS) confirmed a positive effect of user perception of ChatGPT and trust in ChatGPT on the decision-making process and recognition of mental disorders. On the other hand, the study found a negative effect of the decision-making process in recognizing mental disorders. On the other hand, this study’s findings demonstrate that the decision-making process hurts recognizing mental health disorders. This study's results assist the development of policies to improve available digital services, such as ChatGPT or AI, to increase societal awareness. Moreover, by providing empirical evidence from Egyptian entrepreneurs in a developing country context, this study’s findings contribute to the existing psychology, digital technology, and management literature.

Examining factors associated with self-reported cognitive functioning in elite military settings: considerations of combat experiences and post-traumatic stress symptomology

Exposure to traumatic events is associated with post-traumatic stress symptomology (PTSS) in a variety of populations. PTSS is also associated with self-reported everyday cognitive failures, which are acknowledged attentional lapses experienced in one’s naturalistic everyday environment. While a growing literature suggests that PTSS is associated with both traumatic events and attentional functions, little is known regarding similar associations in elite military cohorts, such as Special Operations Forces (SOF) personnel. Herein, we investigate if prior combat experience is associated with everyday cognitive failures, while considering the possible mediating role of PTSS. SOF personnel (N = 119) completed self-report questionnaires assessing prior combat experience, PTSS, and everyday cognitive failures. Direct and indirect associations between these metrics were examined using structural equation modeling. Mediation analyses revealed that the correspondence between combat experiences and everyday cognitive failures is mediated by the severity of subclinical levels of PTSS. Such findings suggest that greater attention to subclinical PTSS is warranted due to its significant association with everyday cognitive failures that may contribute to deleterious mission-related failures in high-demand tactical professionals, such as SOF.

Nearest neighbor future captioning: generating descriptions for possible collisions in object placement tasks

Domestic service robots (DSRs) that support people in everyday environments have been widely investigated. However, their ability to predict and describe future risks resulting from their own actions remains insufficient. In this study, we focus on the linguistic explainability of DSRs. Most existing methods do not explicitly model the region of possible collisions; thus, they do not properly generate descriptions of these regions. In this paper, we propose the Nearest Neighbor Future Captioning Model that introduces the Nearest Neighbor Language Model for future captioning of possible collisions, which enhances the model output with a nearest neighbors retrieval mechanism. Furthermore, we introduce the Collision Attention Module that attends regions of possible collisions, which enables our model to generate descriptions that adequately reflect the objects associated with possible collisions. To validate our method, we constructed a new dataset containing samples of collisions that can occur when a DSR places an object in a simulation environment. The experimental results demonstrated that our method outperformed baseline methods, based on the standard metrics. In particular, on CIDEr-D, the baseline method obtained 25.09 points, whereas our method obtained 33.08 points.

IEat: automatic wearable dietary monitoring with bio-impedance sensing

Diet is an inseparable part of good health, from maintaining a healthy lifestyle for the general population to supporting the treatment of patients suffering from specific diseases. Therefore it is of great significance to be able to monitor people’s dietary activity in their daily life remotely. While the traditional practices of self-reporting and retrospective analysis are often unreliable and prone to errors; sensor-based remote diet monitoring is therefore an appealing approach. In this work, we explore an atypical use of bio-impedance by leveraging its unique temporal signal patterns, which are caused by the dynamic close-loop circuit variation between a pair of electrodes due to the body-food interactions during dining activities. Specifically, we introduce iEat, a wearable impedance-sensing device for automatic dietary activity monitoring without the need for external instrumented devices such as smart utensils. By deploying a single impedance sensing channel with one electrode on each wrist, iEat can recognize food intake activities (e.g., cutting, putting food in the mouth with or without utensils, drinking, etc.) and food types from a defined category. The principle is that, at idle, iEat measures only the normal body impedance between the wrist-worn electrodes; while the subject is doing the food-intake activities, new paralleled circuits will be formed through the hand, mouth, utensils, and food, leading to consequential impedance variation. To quantitatively evaluate iEat in real-life settings, a food intake experiment was conducted in an everyday table-dining environment, including 40 meals performed by ten volunteers. With a lightweight, user-independent neural network model, iEat could detect four food intake-related activities with a macro F1 score of 86.4% and classify seven types of foods with a macro F1 score of 64.2%.

Endocrine-Disrupting Chemicals Exposure and Neurocognitive Function in the General Population: A Community-Based Study.

Endocrine-disrupting chemicals (EDCs) are pervasive in everyday environments. The impacts of these chemicals, along with EDC-related lifestyle and dietary habits on neurocognitive function, are not well understood. The Chang Gung Community Medicine Research Center conducted a cross-sectional study involving 887 participants. From this initial cohort, 120 individuals were selected based on their EDC exposure scores for detailed analysis. Among these, 67 participants aged 55 years or older were further chosen to undergo cognitive impairment assessments using the Ascertain Dementia-8 (AD-8) questionnaire. These 67 older participants did not significantly differ in age, albuminuria, or estimated glomerular filtration rate compared to those with lower impairment scores. This study revealed that mono-(2-ethylhexyl) phthalate (MEHP) levels (8.511 vs. 6.432 µg/g creatinine, p = 0.038) were associated with greater risk of cognitive impairment (AD-8 ≥ 2). Statistical models adjusting for age, gender, and diabetes indicated that MEHP levels positively correlated with AD-8 scores, achieving statistical significance in more comprehensive models (β ± SE: 0.160 ± 0.076, p = 0.042). Logistic regression analysis underscored a significant positive association between high MEHP levels and higher AD-8 scores (odds ratio: 1.217, p = 0.006). Receiver operating characteristic curves highlighted the association of high MEHP levels and EDC exposure scores for significant cognitive impairment, with areas under the curve of 66.3% and 66.6%, respectively. Exposure to EDCs, specifically di-(2-ethylhexyl) phthalate, the precursor to MEHP, may be associated with neurocognitive impairment in middle-aged and older adults.

Do automated vehicle trials test society? Testing mobility futures in the West Midlands

Do automated vehicle (AV) trials in everyday environments like urban streets constitute social tests? This article answers this question with a firm no and yes, based on field study of automated mobility testbeds in the United Kingdom, with a focus on the West Midlands. Engaging with the social studies of testing and automobility futures research, this article examines how AV tests in real-world settings do double duty as tests of society, even as they are marked by social deficits. I develop this analysis by juxtaposing two very different perspectives on automated mobility testing, those of 1) UK AV experts, and 2) residents, artists and researchers who live or work in the West Midlands Future Mobility testbed. I develop two claims. First, I show how real-world testing of automated vehicles remains vehicle-centric, to the point that testing for social aspects for some experts is only conceivable in a simulator. Second, through participatory listening walks I show how AV testing in society raises significant challenges for local communities in the absence of demonstrable benefits in the present. I conclude that the testbed in question does not currently enable the exploration of societal mobility futures, but fieldwork in this environment can help us understand what this would take.