Autonomous vehicles (AVs) can enhance older adults’ mobility and independence, but also present significant safety and usability challenges. In a two-phase formative study, we examine older adults’ (aged 60+) unique design requirements in AVs and the potential of a conversational AI agent inside AVs to support as digital chauffeurs. Phase 1 focus group (N=10) findings present physical AV design with accessible interfaces. We conducted an interview study (N=18) in phase 2 using a virtual reality (VR) simulator and a large-language model (LLM)-powered chatbot. Findings reveal that older adults value conversational agents that could assist during emergencies, initiate social conversations and provide a sense of control and agency. Informed by the outcome, we discuss design implications, emphasizing the need for transparency, clear communication, and chauffeur-like interaction norms in AI agents with appropriate anthropomorphic characteristics (i.e., pacing, tone, and frequency) to ensure a comfortable riding experience for older adults.

While HCI scholarship has investigated how Muslim women navigate their menstruation experiences, there remains a limited understanding of how early menstrual education is sought and shaped by religious upbringing, rulings, and values. Drawing on empirical data from the Asynchronous Remote Communities (ARC) method, we investigate how 14 menstruating cisgender Muslim adults in the US constructed and navigated their understanding of menstrual education. Using menarche (first menstruation) as a probe, we unpack the information they received, the gaps that persisted, and the influence of religious upbringing and narratives on their learning. We found that Muslim women gradually refine their early knowledge of menstruation and rely on social and religious support networks to navigate its implications within religious practice. Building on these insights, we present design implications and opportunities to foster menarche education and contribute to the development of robust, supportive, and religiously aligned menstrual information ecosystems.

The increasing use of social media platforms for seeking religious information raises critical questions about the interplay of technology, algorithms, and faith. Prior HCI literature has explored users’ algorithmic experience in various domains such as news, music, mental health, etc. However, religion, a highly sensitive and significant domain, remains underexplored within the HCI scholarship. Therefore, we investigate how social media algorithms influence users’ religious content engagement through semi-structured interviews and focus groups with 24 Muslim participants from Bangladesh. Our findings reveal that the benefits of algorithms in the Islamic context are often overshadowed by the significant harm of inappropriate recommendations. As a result, participants envisioned algorithms that actively support their spiritual growth and faith practices. Building on these insights, we outline a potential design space for religion-aware algorithms and propose a conceptual framework of users’ hierarchical religious needs for Islamic content to inform future faith-based technology design.

Peer Reviewed

Peer Reviewed

Personal Informatics (PI) tools are crucial in helping individuals monitor their physical health, mental health, and overall well-being. Many Muslim women use multiple PI tools to support their religious and spiritual well-being alongside their health. We investigate the religious and health-tracking practices of Muslim women living in the United States during the month of Ramadan. We conducted a month-long diary study and semi-structured interviews with nine (9) Muslim women observing Ramadan. Through this research, we uncover their motivations for tracking, discover the complex interplay between their social roles and religious practices, and identify conflicts arising from competing objectives (tracking their spiritual and physical health). Our findings contribute insights into the inclusive design of Personal Informatics tools tailored to the needs of Muslim women of faith and provide a call to the research community to expand tracking technologies to include aspects that support religious health and wellness. We discuss design considerations for supporting Muslim women during Ramadan and beyond.

The widespread adoption of menstrual tracking applications has garnered much attention with recent research focusing on inclusive design. However, existing literature has yet to explore the impact of religious practices on menstrual tracking behavior. We investigate the menstrual tracking practices of Muslim women of faith in the United States, a population whose personal reproductive health behaviors are deeply influenced by their faith, values, and religious laws We conducted a three-phase study consisting of preliminary surveys (N=133), semi-structured interviews (N=20), and a post-Roe v. Wade survey (N=77). We highlight motivations for tracking and uncover this overlooked population’s challenges as they engage with menstrual tracking technologies. We reveal an intimate connection between menstrual tracking and religious practices. We uncover challenges from engaging with existing menstrual tracking applications and contribute design recommendations for accommodating faith in the design of health tracking technologies. We amplify a call to action for the HCI community to reduce the "othering" of under-represented populations and to better support the inclusive design of technologies that center religious identities and values for individuals of faith.

Professional designers often struggle to apply insights from HCI research in their work. To make academic knowledge more accessible to practitioners, HCI researchers have created translational design tools, such as design cards, that support the translation of research insights into design practice. Prior work explored design cards for behavior change, interaction design, personal health informatics, and the sharing economy. Our work complements prior research by exploring the design and use of translational design cards for social aspects of societal resilience through a two-stage study with 14 student designers and eight professional designers. Our findings provide an empirical understanding of the design cards’ generative value for incorporating research insights into the design process. Additionally, we discuss recommendations and highlight opportunities to enhance the design and use of the cards beyond societal resilience.

Researchers are increasingly facilitating qualitative research studies online. While this has made research more accessible for participation, there have been notable encounters with “fraudulent” participants. By fraudulent, we refer to individuals who are deceptive about meeting the inclusion criteria, their identity, or experiences. Fraudulent participants have generated new challenges for researchers who have to interact 1:1 with these individuals, face ethical dilemmas on appropriate next steps, diagnose and prevent the issue from happening again, and deal with their own identity as a scholar. In this study, we interview 16 HCI researchers to understand and learn from their experiences. We contribute: (1) an understanding of how HCI qualitative researchers deal with fraudulent participants; (2) a guide for qualitative HCI researchers on how to handle fraudulence; and (3) a reflection on how the HCI research community might better improve our science and training efforts.

The COVID-19 pandemic has threatened disproportionately rural older adults’ health and well-being as they suffer from unique social exclusion due to a lack of services, such as transportation, communication infrastructure, healthcare, and social services. Although older adults can uniquely cope with pandemic adversity compared to younger adults, less attention has been directed to investigating the coping and resilience of rural older adults. To understand how diverse coping strategies impact the resilience of rural older adults, we conducted interviews with 26 rural and small-town older adults. Older adult participants adopted different coping strategies, such as following protective measures, keeping themselves busy, providing and receiving social support, and having a positive mindset. They experienced positive changes, such as increased interpersonal connectivity. Older adults’ individual-level coping processes are influenced by their social and physical environments. We explore design opportunities to support older adults’ resilient practices and harness their skills to facilitate community resilience.

Research on aging technologies typically has explored health condition management and physical activity, while other aspects of active aging (e.g., psychological and social well-being) receive less attention. To better support active aging, we focus on the context of tracking technologies because half of the U.S. aging population engaged in keeping records of health and non-health information using manual and digital mediums. We interviewed 18 older adults to investigate their holistic tracking practices. We found participants were motivated to manage their everyday life tasks, preserve sentimental values, generate knowledge for broader audiences, and support relationships and caregiving. These motivations can help older adults age actively by supporting multi-dimensions of well-being besides physical health. Reflecting on findings, we discuss design considerations for tracking technologies to support active aging by expanding the current focus on supporting physical health to broader psychological and social well-being.

Research on smart home monitoring for older adults has predominantly focused on systems whose data and alerts are directed towards family members, caregivers, or healthcare providers. Older adults have expressed interest in engaging with these systems by seeing and using their data, but they are often limited to a passive role as subjects of monitoring. This paper presents qualitative results of a longitudinal smart home project with older adults living independently in the community. Based on interviews conducted throughout the 2.5-year study with 12 participants, we report on their lived experiences of having the monitoring system in their homes and on how they reflected on the data collected by the system. The results show how participants were able to extract meaningful information from the monitoring data without finding the system invasive or intrusive. Specifically, older adults exhibited interest in data that they found indicative of living an active lifestyle, such as time spent outside the home. Drawing from critical literature on active aging, we discuss implications for incorporating peer comparisons to support reflection on personal health data without reinforcing a deficit narrative of aging.

Aging in place technologies are designed to extend independence and autonomy in older adults, promoting quality of life and peace of mind. Prior work has described how adoption and continued use of these technologies are low when they are perceived as reinforcing aging stigma or reminding older adults of negative aspects of aging. Building on past research, we investigate older adults’ perceptions of four different kinds of aging in place technologies. Based on in-depth interviews with 18 older adults using a set of scenarios, we describe how different design characteristics contribute to perceived aging stigma. Additionally, we draw on Duner & Nordström’s classification of coping strategies to discuss how specific kinds of technology can best support older adults during different stages of the aging process.

The COVID-19 pandemic led to dire consequences globally, and it has been particularly challenging for older adults. They are at a higher risk of adverse outcomes of the disease \citecdc_covid. Older adults also use less technology than other age groups \citetech_old, so they mostly rely on in-person interactions and services for social support. However, disease mitigation efforts such as social distancing and self-quarantining severely limited in-person interactions, hindering older adults' social support during the COVID-19 crisis. In this paper, we present findings on social support realities from semi-structured interviews with older adults (N=15) living alone in community dwellings. We found that older adults' support roles, support sources, and support concerns evolve as they passed through this time of sweeping change. They are enthusiastic about providing support to people who are older and more vulnerable than themselves. At the same time, their needs for safety, autonomy, and independence create tensions around social support. We propose a framework to illustrate the evolving ecology of social support that can facilitate the holistic design of socio-technical support systems for older adults. We argue against the societal portrayal of older adults as vulnerable individuals. Rather, there is an opportunity to design support systems considering them as anchors in society. Towards that goal, we present design implications for future socio-technical support systems to empower older adults to age in place during a crisis.

When studying technologies pertaining to health, wellness, accessibility, and aging, researchers are often required to perform a balancing act between controlling and sharing sensitive data of the people in their studies and protecting the privacy of these participants. If the data can be anonymized and shared, it can boost the impact of the research by facilitating replication and extension. Despite anonymization, data reporting and sharing may lead to re-identification of participants, which can be particularly problematic when the research deals with sensitive topics, such as health. We analyzed 509 CHI papers in the domains of health, wellness, accessibility, and aging to examine data reporting and sharing practices. Our analysis revealed notable patterns and trends regarding the reporting of age, gender, participant types, sample sizes, methodology, ethical considerations, anonymization techniques, and data sharing. Based on our findings, we propose several suggestions for community standards and practices that could facilitate data reporting and sharing while limiting the privacy risks for study participants.

Demand for fast data sharing among smart devices is rapidly increasing. This trend creates challenges towards ensuring essential security for online shared data while maintaining the resource usage at a reasonable level. Existing research studies attempt to leverage compression based encryption for enabling such secure and fast data transmission replacing the traditional resource-heavy encryption schemes. Current compression-based encryption methods mainly focus on error insensitive digital data formats and prone to be vulnerable to different attacks. Therefore, in this paper, we propose and implement a new Huffman compression based Encryption scheme using lightweight dynamic Order Statistic tree (HEliOS) for digital data transmission. The core idea of HEliOS involves around finding a secure encoding method based on a novel notion of Huffman coding, which compresses the given digital data using a small sized "secret" (called as secret_intelligence in our study). HEliOS does this in such a way that, without the possession of the secret intelligence, an attacker will not be able to decode the encoded compressed data. Hence, by encrypting only the small-sized intelligence, we can secure the whole compressed data. Moreover, our rigorous real experimental evaluation for downloading and uploading digital data to and from a personal cloud storage Dropbox server validates efficacy and lightweight nature of HEliOS.

Increased processing power of MapReduce clusters generally enhances performance and availability at the cost of substantial energy consumption that often incurs higher operational costs (e.g., electricity bills) and negative environmental impacts (e.g., carbon dioxide emissions). There exist a few greening methods for computing clusters in the literature that focus mainly on computational energy consumption leaving cooling energy, which occupies a significant portion of the total energy consumed by the clusters. To this extent, in this paper, we propose a machine learning based approach named as Green MapReduce Cluster (GMC) that reduces the total energy consumption of a MapReduce cluster considering both computational energy and cooling energy. GMC predicts the number of machines that results in minimum total energy consumption. We perform the prediction through applying different machine learning techniques over year-long data collected from a real setup. We evaluate performance of GMC over a real testbed. Our evaluation reveals that GMC reduces total energy consumption by up to 47% compared to other alternatives while experiencing marginal throughput degradation in a few cases.

Ubiquitous heterogeneous wireless networks demand a general-purpose multi-objective vertical hand-off mechanism taking into account network dynamics as one of its decision attributes. However, to the best of our knowledge, such a mechanism is yet to be devised. Therefore, in this paper, we propose a new multi-objective vertical hand-off (MOVH) mechanism that takes into account network dynamics as one of its decision attributes. We customize Multi-Objective Genetic Algorithm (MOGA) in this regard. The results obtained from our numerical simulation demonstrate that the proposed mechanism yields better scalability and stability. We also evaluate the performance of our MOVH mechanism against two most popular alternatives: GRA and TOPSIS. This evaluation comprises of both test-bed experiments and ns-2 simulation. The results from both test-bed experiments and ns-2 simulation demonstrate that the proposed MOVH mechanism has significant performance improvement over both GRA and TOPSIS.