Various programs have been established to extend and promote healthcare services to low-income rural Americans facing financial barriers. These programs offer a wide range of healthcare services at little to no cost. However, despite the availability of these services, challenges arise in bringing patients to healthcare providers in a timely manner for healthcare. Many individuals in rural communities are unable to receive recommended services, primarily due to transportation challenges. These challenges, in turn, significantly impact their care-seeking behaviors and hinder their access to timely healthcare. Patient no-shows not only delay diagnoses but also result in lost revenue for healthcare providers due to under-utilization of medical facilities and staff resources. To address these issues, the ongoing mobility transformation fueled by on-demand services may offer affordable transportation solutions to improve healthcare access and reshape care-seeking behaviors among people in under-served communities. This project focuses on West Alabama, specifically the health-disadvantaged Black Belt region, to investigate preventive care-seeking behaviors. The primary objective is to explore the impacts of on-demand mobility services on these behaviors among under-served individuals.

This project will create a Smart and Connected Rural Community (SCRC) comprised of interdisciplinary researchers, healthcare providers, transportation providers, and community stakeholders to promote preventive healthcare in rural communities through deploying on-demand mobility services. This project will conduct integrative research with objectives covering both technological and social science dimensions: 1) Technological Dimension – By partnering with local health providers and transportation providers, this project will develop a Smart Health and Mobility System (SHMS) to leverage and coordinate existing transportation resources within the study area to offer on-demand mobility services to patients and doctors/nurses in need of traveling between medical facilities and patient homes; 2) Social Science Dimension – Through community engagement activities (e.g., meetings, focus groups, and surveys), this project will explore the impacts of on-demand mobility services on preventive care-seeking behaviors among uninsured or under-insured patients. This project will significantly advance the knowledge by exploring the impacts of mobility services on care-seeking behaviors in disadvantaged rural communities. The knowledge will aid healthcare providers and stakeholders make informed decisions to leverage emerging mobility services to deliver cost-effective healthcare services and improve quality of life in disadvantaged rural communities. The project is expected to set the groundwork for implementing on-demand mobility solutions to enhance healthcare in these areas.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Despite considerable efforts in combating substance use and exploring novel treatment and recovery strategies, current practices have not been able to connect many patients from underserved low-income communities in rural areas with available healthcare resources due to infrastructure challenges, inaccessibility to pharmacies, patient’s inability to drive, and lack of transportation. Responding to such issues involves interactions among diverse community stakeholders, including healthcare providers, research institutes, government agencies, nonprofit organizations, and community residents. However, these stakeholders are typically isolated or inefficiently organized, which leads to unbalanced community coordination and inefficient decision-making in substance use responses. To address these challenges and gaps, this Smart and Connected Communities Panning Grant (SCC-PG) will go beyond the current practices to promote substance use disorder treatment by connecting multiple community stakeholders via innovative community-based coordination mechanisms and connecting the residents from rural areas with urban medical resources via novel mobility technologies. Situated at the heart of Appalachia, Knox County and the surrounding communities are selected as an ideal natural testbed to demonstrate how the proposed delivery mechanism and framework can address infrastructure challenges, hurdle interaction and communication barriers, and help improve access to necessary medications among individuals with substance use disorder.

The goal of this SCC-PG project is to create connected systems and intelligent technologies to advance the understanding of interactions and perceptions among people who use drugs, healthcare providers, and government agencies; and build upon the in-depth understanding to engage communities to enable novel practices to treat substance use disorder and reduce illicit drug use. Specifically, the project aims to 1) develop community perception models that reveal how patients’ choices and concerns and public’s acceptance on truck- and drone-enabled delivery mechanisms influence the service patterns and operations, 2) create new truck- and drone-assisted healthcare delivery frameworks and operations through integration of these quantified perception models and operation constraints from healthcare providers and regulatory agencies, and 3) establish novel community engagement models that channel information in a connected way, through which patients, healthcare providers, scientists, engineers, government officials, and volunteers are all involved in a timely fashion to foster informed and all-inclusive decisions and practices to achieve connected interventions and treatment. This research is expected to lay a foundation for more comprehensive design and control of innovative mobility systems and connected and collaborative frameworks that span social and technical dimensions with community engagement to improve medication access of underserved people with substance use disorder.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

For both parents and educators, monitoring and adjusting their behaviors to ensure that children develop appropriate prosocial and learning behaviors is a complex balance between nurturance and limit setting. When these interactions are strained, negative or coercive cycles may emerge that delay appropriate development and exacerbate existing impairment. To disrupt the development of coercive cycles, adults must have the ability to accurately assess the quality of their interactions with children and integrate this information into personal change. Approaches to measuring these types of interactions will inform what we know about the mechanisms of child social, emotional, and learning development in STEM learning settings, and enable the creation of adaptive interventions for those moments when support is most needed. This project envisions a closed-loop intervention framework to promote a supportive and interactive environment around children. Smart wearables will sense interaction and responses between the children and their parents or educators, using embedded machine learning technology to recognize supportive behaviors. The perceived behaviors will be sent to a cloud server where adaptive interaction strategies will be identified from either online psychological consultation or artificial intelligence. These interaction strategies will then be provided to the parents and educators in the form of guidance cues to promote a supportive STEM learning environment around the children.

This planning project aims to understand the barriers and critical problems in the implementation of smart technology and psychological strategies to support adult-child interactions in STEM learning settings. The work will proceed by convening key stakeholders (parent organizations, formal educational institutions, and informal educational institutions) in a series of iterative discussions to produce a set of adult-child behavioral targets that are essential to children’s development of social, emotional, and learning skills. Further discussions will then identify mechanisms to enhance these behaviors, and reduce competing, less effective approaches. Qualitative thematic analysis of the discussions will be used to capture these behaviors and mechanisms. Then technologies will be developed to measure, provide feedback on, and improve these behaviors. These devices will be piloted with adult-child dyads. Audiovisual data collected by the devices will be human coded as well as processed by algorithms to vet the technological capacity of the devices to detect and respond to targeted behaviors. A series of debriefing interviews and surveys with adult-child dyads will be used to determine the feasibility, acceptability, and utility of the devices. The collected preliminary data will support the forming of critical technological and social science research questions that co-inform one another: questions about the social engagement between adults and children will drive the technical research, and what can be discovered via the technological research will open up new questions that can be posed about social engagement between children and adults. Adult-child interactions are key social factors that integrate to produce student social, emotional, and academic outcomes. Within our informal educational communities, our formal educational communities, and our familial communities it is essential to find the best mechanisms for measuring, providing feedback, and improving these interactions. This work thus seeks to advance a new approach to, and evidence-based understanding of, the development of STEM learning. This Smart and Connected Communities project is also supported by the Advancing Informal STEM Learning program, which seeks to (a) advance new approaches to and evidence-based understanding of the design and development of STEM learning in informal environments; (b) provide multiple pathways for broadening access to and engagement in STEM learning experiences; (c) advance innovative research on and assessment of STEM learning in informal environments; and (d) engage the public of all ages in learning STEM in informal environments.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This NSF Smart and Connected Community (S&CC) planning grant will set the groundwork for exploring a sustainable vertiport system capable of deploying autonomous drone swarms for methane emission measurements over orphaned wells. The planning grant will also scrutinize the responses of regulators and operators to the potential technological changes. These abandoned oil or gas wells, typically left behind by the fossil fuel extraction industry when operating expenses outstrip production rates, contribute significantly to greenhouse gas emissions. Given the high costs associated with the plugging, remediation, and restoration of these wells, robust, data-driven evidence is required to justify and prioritize the allocation of state and federal funds. Traditional methane emission measurements, involving flux chamber installation at each open wellhead, carry high capital and operational costs and are challenging to deploy in hard-to-reach areas. While primarily targeting the oil and gas industry community, the research outcomes could offer valuable insights applicable to diverse areas such as wildlife monitoring, anti-poaching initiatives, infrastructure and aircraft inspections, construction site surveillance, and water pollution monitoring.

The research aims to create a new cross-domain framework for an integrated, sustainable vertiport that aids an autonomous drone swarm inspection system. The project revolves around three technical objectives: 1) Developing a low-cost, portable, and sustainable vertiport to facilitate precision landing and housing, protection, and recharging of multiple drones; 2) Constructing a safe federated deep reinforcement learning algorithm to enable drone swarm landing and takeoff in harsh environments; 3) Examining three-dimensional drone swarm path planning for efficient methane plume localization and emission quantification. Simultaneously, the project will pursue two social science objectives: 1) Quantitative assessment of potential efficiency and equity improvements in federal funds allocated for cleaning up orphaned wells; 2) Encouraging operators to adopt this cost-effective monitoring system by enhancing equity in carbon dioxide sequestration tax incentives. The research outcomes will revolutionize measurement and monitoring technologies, enabling the oil and gas industry to identify economically viable and sustainable solutions to reduce greenhouse gas emissions, while providing valuable insights and tools applicable to high-impact areas such as airborne wireless edge computing and autonomous drone swarm defense.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Children with autism spectrum disorder (CWA) often engage in severe problem behavior, and thus require long-term care with individualized clinical assessment, treatment, and intervention. Applied behavior analysis (ABA) therapy, considered an evidence-based best practice, can be time-consuming, resource intensive, and prone to human bias. This Smart and Connected Communities Planning Grant (SCC-PG) project seeks to co-design an AI-based solution for collecting behavior data for automatic measurement of severe behaviors (e.g., frequency, intensity, latency, etc.), assisting diagnosis and treatment decisions, and communicating to patients and families in real-time for early intervention.

The project team partners with a range of community stakeholders in a pilot study in San Antonio, TX for coordinated care of CWA through an AI-augmented platform. The planning process begins with identifying key platform parameters including data types, privacy concerns, main function modules, and expected performance. Different AI algorithms are examined for fusing multi-modal data to inform ABA. Post-survey and focus groups are conducted to uncover the effects of incorporating the IoT-Edge-Cloud prototype into clinical practices. The project has the potential to improve health outcomes for autistic children and the well-being of their families.

Older adults strive to be independent and healthy yet compared with younger individuals, they are at greater risk of chronic health conditions and social isolation. Solutions are needed that create ways for older adults to thrive, connect, contribute to, and shape their communities. The increasingly available Internet of Things (IoTs), particularly wearables and smart home devices (e.g., smartwatches, voice-activated home assistants), offer important opportunities for real-time interventions for self-management of care, especially if they are customized to meet each person’s unique needs and goals. This planning project focuses on older military veteran populations in Phoenix, Arizona. The project is building a multidisciplinary team to form a research agenda for studying, developing, and deploying an IoT based solution for providing high quality, low cost, and community-sensituve self-managed care. This project can benefit Arizona’s large populations of older adults and veterans, with potential to impact underserved aging populations across the U.S. The project also provides education and workforce development to all levels of students from multiple disciplines, and in particular creates innovations and access for many underrepresented students at Arizona State University, where the project is based.Through focus groups and pilot data collection, this planning project is laying the groundwork for innovations in real-time and privacy-preserving learning systems which that employ IoTs and edge computing to support learning on live, personal health data and provide real-time, personalized feedback without compromising user privacy. The project also entails heterogeneous data integration and learning techniques to detect social-emotional, and health changes and support prevention and early interventions. The project builds on human-systems engineering and health informatics. This approach is designed to meet the diverse needs of older adults and to allow them to more effectively take charge of their health; The project includes an analysis of social, cultural, and relationship factors and their effects on cognitive and affective processes in older veterans.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Unsheltered homelessness has grown at staggering rates, particularly across West Coast cities such as San Diego. Unsheltered people are at higher risk than the general population of experiencing flooding risk, as they are more likely both to be living in the most flood-vulnerable locations as well as disconnected from existing flood warning systems. This predicament results in unequal disasters and environmental impact, burdening the most vulnerable people with the least information in critical moments. From a technical aspect, the absence of an intelligent system for flood detection causes inaccuracy in prediction and delays in responses. This project aims to overcome these challenges by integrating imagery data into the current state-of-the-art flood data acquisition and urban infrastructure modeling. The broader impact of the study involves raising community awareness about the use of new smart technologies and supporting proactive flood and emergency management by engaging residents, businesses, and practitioners in the development of the research program. Involved students will have the opportunity to learn multi-disciplinary research topics through engaging in collaborative teamwork. The new smart system and connected decision-making framework will be transferable to other flood-prone communities across the U.S. West and East Coasts that are confronting more frequent floods as a result of climate change coinciding with an unprecedented housing affordability crisis. This project will create a diverse, multidisciplinary community of researchers, practitioners, and concerned citizens to develop novel technologies and integrated theories and methods to improve the time and accuracy of flood data acquisition, detection, and monitoring. A set of deep learning-based image processing systems will be developed and trained using large fully segmented flood image datasets to detect formation and monitor flood events. A flood model will be developed to simulate and forecast real-time floods and impacts. This project will enhance the response time and accuracy of flood early warning and monitoring systems to support the resilience and emergency management of smart and connected coastal communities. During the planning phase, a Community Advisory Group will be assembled and convened to advise on the development of the intelligent flood system and guide the human subject’s data collection activities, including structured interviews with unsheltered people. This project enhances the knowledge needed to support smart and connected communities by including 1) novel data collection techniques from various sources of information, such as ground-based cameras, 2) artificial intelligence-based flood visual sensing and analyzing diverse data, and 3) substantive community engagement that centers the needs of unsheltered people.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Smart city platforms–encompassing mobile apps, cameras, sensors, algorithms, and predictive analytics—generate troves of data on residents. Research suggests that excessive surveillance reinforces a sense of insecurity and leads residents to fear civil liberties violations, particularly among communities of color. Our digital rights platform will empower community members by granting them agency over how the City collects, uses and stores their personal data. The platform will be designed collaboratively with hundreds of Long Beach residents participating in a civic user testbed and other qualitative data collection. The platform will feature text and open-source iconography that visually conveys how the City of Long Beach uses specific technologies, what data these technologies collect and how the City utilizes that data. We plan to strategically deploy signage across Long Beach, physically adjacent to or digitally embedded within civic technologies, e.g., sensors, cameras, mobile payment kiosks, a 311 app. The platform will include a QR code or hyperlink that take users to an online dashboard where they may learn additional details, update data collection preferences, and share comments and concerns with local officials—giving residents a clear understanding of how local government collects, analyzes, shares, and retains their personal data. This digital rights platform will feature text and the open-source iconography that visually conveys how the City of Long Beach uses specific smart technologies, what data these technologies collect and how the City utilizes that data. The platform considers the technical, legal, ethical, and spatial aspects of smart technologies. Grounded in frameworks of trust and contextual integrity, the project is focused on the City’s vision to use data in ethical ways that avoid reinforcing existing racial biases and discriminatory decision-making. Specifically, we plan to strategically deploy signage across Long Beach, physically adjacent to or digitally embedded within civic technologies, e.g., sensors, cameras, mobile payment kiosks, a 311 app. The platform will include a QR code or hyperlink that take users to an online dashboard where they may learn additional details, update data collection preferences, and share comments and concerns with local officials. We plan to work with smart city technology developers to create a software solution that will, ultimately, enable residents to opt out of data collection. The project will inform novel accountability strategies meant to ensure that wildly disparate smart city technologies—each employed for a distinct purpose—respect residents’ data privacy and avoid discriminatory impacts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This NSF Smart and Connected Communities project will employ a novel and multidisciplinary approach informed by community participation to detect, map, and analyze “near-miss” events that occur when a collision between a vulnerable road user, such as a bicyclist or pedestrian, and an automobile is narrowly avoided. Rising injury and fatality rates in the United States for vulnerable road users is an area of societal concern, and contribute to public hesitancy to walk or bicycle more. These trends challenge ongoing efforts nationwide that aim to both make roads safer for all and reduce transportation sector emissions through a modal shift to increased walking, bicycling, and transit use. To date, data-driven solutions to address issues related to vulnerable road user safety often rely on official crash data, but these data cannot alone comprehensively represent the safety experiences of vulnerable road users. The ability to more broadly record near-miss events, and how their frequency and locations compare to officially reported crash data, is essential to informing safety-oriented transportation planning strategies. To address this topic, this project will integrate approaches and technological innovations from geography, traffic engineering, and urban planning, in partnership with community collaborators in greater Reno and Sparks, Nevada.Recent advancement in classification techniques applied to data collected from Light Detection and Ranging, or LiDAR, sensors provides an ability to detect near-miss events involving vulnerable road users. This project will deploy a portable network of such sensors at locations throughout greater Reno and Sparks. Sensor locations will be informed by responses to a web-based survey distributed to those who frequently walk or bicycle in the community that will prompt them to identify specific locations of vulnerable road user safety concern. Data will be collected at these locations for one week. Emerging near-miss detection methods will be applied to the field-collected data, and frequency and type of near misses will be compared against official crash data. A community focus group will review near miss events detected by these sensors and provide feedback to improve event identification methods. A Geodesign workshop will produce a collaborative plan that will prioritize locations for future assessment of vulnerable road user safety, and identify potential countermeasures. These efforts will help guide ongoing efforts to integrate a sensor network that if effectively scaled, could improve the ability to detect near-miss events in real-time, which in turn can better inform planning efforts to improve road user safety.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Approximately 91% of the world population lives in environments that do not currently meet air quality standards. In the United States (U.S.), the Clean Air Act of 1970 has resulted in air pollution concentrations dropping below national standards, meaning that most communities in the U.S. have cleaner air. However, clean air is not realized across all communities, especially in communities of color, where air quality can differ significantly. Further, regulatory air quality sensors that are sparsely deployed may not accurately detect the quality of air that residents breathe in their communities. With the availability of low-cost sensors and advancement of low-cost single-board computers and microcontrollers, this research aims to provide residents with an ability to accurately understand their air quality through the deployment of an Internet of Things (IoT) air quality sensor. We will meet with residents that have been affected by both redlining and nearby pollution sources to better understand how air quality affects their daily lives and what air quality information is most beneficial to them. In addition, the team will closely collaborate with partner school(s) to create K-12 curriculum for students to learn how to create their own air quality sensor, deploy it at their school, and make the air quality readings publicly available.

In this research, we will combine the availability of low-cost particulate matter sensors with the accessibility of IoT compatible single-board computers and microcontrollers to enable publicly available fine-grained air quality information. To provide real-time access to the data, a prototype mobile application for both iOS and Android, along with a web dashboard, will be developed. To address common challenges of both power and connectivity, we will partner with PCs for People to deploy the sensors and provide connectivity through their existing infrastructure. An enclosure will be developed that ensures proper airflow, has low interference with wireless communication, and is modular to allow other sensing capabilities in the future. We will compare the findings from a test deployment of the sensors with regulatory sensors readings and share the results with the community and local officials. To ensure the sustainability of the project and provide an opportunity for it to expand, we will create an open-source Computer Science and Engineering curriculum in partnership with a local middle school and we will pilot a tech camp at our university.