Sexual and Reproductive Health (SRH) education remains largely limited to awareness-based interventions, particularly in stigmatized socio-cultural contexts. Researchers are extensively focusing on pedagogical theories, reshaping teaching and learning practices to enhance educational outcomes. This paper investigates the application of Bloom’s taxonomy as a design framework for scaffolding SRH learning from foundational knowledge to higher-order cognitive engagement. We conducted a mixed-methods study with 45 participants who engaged in learning activities across different cognitive levels, followed by semi-structured interviews. Our analysis shows that integrating higher-order learning objectives—such as application, analysis, and reflection—supports deeper engagement and understanding of SRH topics beyond surface-level awareness. We also highlight how technologies such as VR and LLMs enable flexible, exploratory learning and create safe, judgment-free spaces for engaging with sensitive topics, supporting users’ comfort. Based on these findings, we derive design implications for developing structured, inclusive, and stigma-sensitive SRH education technologies. This work contributes to HCI by empirically demonstrating how established learning theories can advance learning in sensitive educational domains.

HCI has long engaged with designing technologies for marginalized communities, increasingly foregrounding environmental and social justice questions. This paper examines air pollution as structural violence, disproportionately affecting marginalized populations. Drawing on semi-structured interviews (n=25) with daily wagers-one of the most vulnerable urban populations, we investigate how they experience, interpret, and respond to pollution-related risks amidst information gaps and misinformation. Our analysis reveals critical shortcomings in ICT-based information dissemination, which often fails to address the needs of vulnerable groups due to challenges of trust, language, and cultural alignment. In response, we identified key design requirements and examined the suitability of IVR and WhatsApp as accessible channels in this context. We developed and evaluated (n=10) prototypes that can deliver trusted, relevant, and actionable information. We contribute an understanding of how HCI can help design resilient information infrastructures that serves vulnerable voices and strengthen community preparedness in the face of environmental crises.

Menstrual experiences are shaped by many different stakeholders, entities, and the broader socio-cultural context, or in other words, the ecological systems surrounding a menstruator. We explore what it means to take an ecological approach in studying and designing menstrual technologies. We translated and adapted Ecological Systems Theory (EST) for menstrual wellbeing and packaged the outcome in the form of a socio-ecological Canvas, accompanied by written examples and a set of prompts to facilitate engagement. We invited ten experts to engage with the Canvas in reflective workshops, which informed its further refinement. These sessions highlighted the Canvas’ generative value, fostering critical reflection on how design choices are shaped by and ripple across layers of influence. With this translational research, we invite HCI researchers and practitioners to critically reflect on the ecologies they study and design for, envisioning both aspired versions of existing realities and realities that do not exist yet.

The rapid growth of large language models (LLMs) is reshaping access to health information, yet their role in sexual and reproductive health (SRH)—a highly stigmatized and sensitive domain is a nascent area of research, particularly in the Indian context. This study investigates how individuals perceive and engage with an LLM-based chatbot for SRH, examining trust boundaries, privacy expectations, and the cultural dimensions of LLM-mediated health communication. We designed a Retrieval-Augmented Generation (RAG) chatbot for sexual health and conducted a user study in which fifteen participants interacted with the system, followed by semi-structured interviews. Our findings reveal three interconnected themes: First, LLMs serve as low-stakes knowledge companions that reduce the cognitive and social burden of SRH information-seeking; secondly, privacy needs are relational and situational rather than uniform, shaped by domestic contexts and family structures; and lastly, LLM responses exhibit systematic gender bias in SRH framing—a pattern which carries significant implications for equitable chatbot design. We contribute design recommendations for culturally sensitive, privacy-aware, and gender-equitable LLM systems for SRH communication.

Virtual Reality (VR) is reshaping multimedia learning enabling immersive and experiential learning environments. While VR inherits the two core components of traditional 2D multimedia learning - verbal and visual content, research suggests that the principles governing these components in 2D multimedia environments cannot be directly applied to Virtual Reality Learning Environments (VRLEs) owing to the additional affordances offered by the medium. In this work, we examine the applicability of two key principles which address the representation of verbal content in 2D learning environments, the Modality Principle and the Redundancy Principle, within the context of VRLEs. We conducted a study with forty participants in which instructional material—covering factual, conceptual, and procedural content—was delivered across four modality conditions: text-only, audio-only, text + audio with full redundancy, and text + audio with partial (concise) redundancy. The results indicate a clear preference for redundant text-plus-audio presentations, particularly for procedural tasks, with no evidence that redundancy increases cognitive load—contrary to traditional findings from comparable 2-D contexts. Audio‑only, by contrast, imposed the highest load in procedural learning. These outcomes underscore content type as a critical moderator and point to the limits of directly importing 2‑D design rules into VR. Our findings further highlight the role that VR-specific evaluation frameworks and learner familiarization play in VR instructional design.

Emotion recognition from physiological signals has substantial potential for applications in mental health and emotion-aware systems. However, the lack of standardized, large-scale evaluations across heterogeneous datasets limits progress and model generalization. We introduce FEEL (Framework for Emotion Evaluation), the first large-scale benchmarking study of emotion recognition usingelectrodermal activity (EDA) and photoplethysmography (PPG) signals across 19 publicly available datasets. We evaluate 16 architectures spanning traditional machine learning, deep learning, and self-supervised pretraining approaches, structured into four representative modeling paradigms. Our study includes both within-dataset and cross-dataset evaluations, analyzing generalization across variations in experimental settings, device types, and labeling strategies. Our results showed that fine-tuned contrastive signal-language pretraining (CLSP) models (71/114) achieve the highest F1 across arousal and valence classification tasks, while simpler models like Random Forests, LDA, and MLP remain competitive (36/114). Models leveraging handcrafted features (107/114) consistently outperform those trained on raw signal segments, underscoring the value of domain knowledge in low-resource, noisy settings. Further cross-dataset analyses reveal that models trained on real-life setting data generalize well to lab (F1 = 0.79) and constraint-based settings (F1 = 0.78). Similarly, models trained on expert-annotated data transfer effectively to stimulus-labeled (F1 = 0.72) and self-reported datasets (F1 = 0.76). Moreover, models trained on lab-based devices also demonstrated high transferability to both custom wearable devices (F1 = 0.81) and the Empatica E4 (F1 = 0.73), underscoring the influence of heterogeneity. Overall, FEEL provides a unified framework for benchmarking physiological emotion recognition, delivering insights to guide the development of generalizable emotion-aware technologies. Code implementationis available at https://github.com/alchemy18/FEEL. More information about FEEL can be found on our website https://alchemy18.github.io/FEEL_Benchmark/.

Advances in virtual reality (VR) are transforming interaction paradigms by shifting towards gesture-based control driven by physiological sensing, enabling more intuitive and embodied experiences. Surface electromyography (sEMG) is emerging as a reliable modality for this hands-free and expressive gesture recognition in VR. However, prolonged mid-air gestures can lead to muscle fatigue and physiological changes that degrade overall recognition performance. Further, this degradation is not uniform across gestures which can impact user performance and experience in VR applications. While existing literature has shown that fatigue alters sEMG signals, its effects during extended immersive interaction and across various gestures remain underexplored. We conducted a 35-participant study in which each participant continuously performed five gesture in VR for 20 minutes each, while we collected high-resolution sEMG data from eight forearm sensors and real-time subjective fatigue ratings using the Borg CR10 scale. Further, we evaluate how gesture recognition models behave under fatigue and explore the impact of incorporating both objective (signal-derived) and subjective (user-reported) fatigue features into classification models. Our results show that integrating fatigue signals enhances model robustness and improves recognition accuracy during extended use.

ChatGPT has been gaining immense popularity in various domains, including healthcare. Prior studies have explored ChatGPT's potential in managing certain health conditions and supporting clinicians in their practice but with a focus outside the Global South. We aim to fill this gap by understanding how ChatGPT supports the needs of marginalized populations residing in India around family planning. Rural Indian women have high unmet needs for family planning and spacing, and further, with the associated taboo, these women face challenges in seeking support. Thus, we focus on understanding how ChatGPT operates in the support-seeking network of postpartum women residing in resource-constrained areas of Northern India. We conducted Zoom sessions with 8 participants to understand their engagement with ChatGPT using screen-sharing. We observed how ChatGPT could/ could not adapt to our participants' language considerations, given their limited literacy levels. We studied our participants' experiences with framing questions to ChatGPT and identified their strategies for evaluating the correctness of ChatGPT's answers. Further, we gathered participants' struggles with the voice-conversation feature of ChatGPT, a feature traditionally seen as a way of bridging the literacy gap. Finally, we offer recommendations for making ChatGPT more useful among a low-resource population for their needs on a healthcare topic considered taboo in rural India.

Despite the Indian government's efforts to improve the awareness and uptake of family planning, rural women have an unmet need for family planning. Previous research, primarily in the medical domain, has offered counseling and informational support for family planning in a 1:1 manner and has explored non-technological-based peer support interventions for sexual and reproductive health in general. However, in HCI and CSCW, family planning remains an understudied topic. Thus, we aim to contribute to HCI knowledge by studying family planning among marginalized women in India. We explore the potential of technology-based peer support for family planning, given its high feasibility and acceptability for managing health conditions such as mental health illnesses and HIV. We conducted surveys, interviews, and a three-week field experiment with 30 rural Indian women to understand their engagement in a WhatsApp-based peer support group. Given the limited support within the family and from doctors and community health workers, we found the group to offer informational and emotional support to peers, help with local resources, and contribute to knowledge gain. Participants' feedback uncovered design suggestions such as the design of discussion prompts, the role of the moderator, and privacy levels. We contribute design considerations for designing a peer support group for family planning and discuss the need to design a suite of tools for family planning.

With a large and growing population, India has invested significantly over the last 70 years in its National Programme for Family Planning. Despite a sustained focus on family planning, uptake of family planning practices is limited, especially for rural women. Previous research posits an awareness-intention-usage gap among rural women, and in this work, we aim to understand this gap. We conducted three FGDs, surveys, and twenty interviews to explore rural Indian women's practices relating to contraceptive usage and adoption, support received within and outside of the family, factors influencing decision-making, and aspirations from technology to meet their needs. Our findings uncover limited knowledge about contraception and body, along with insufficient counseling support from families and healthcare workers. Our findings highlight participants' desire for experiential knowledge and present the privacy levels they need. Through our work, we aim to extend the postpartum ecology, with a focus on family planning, which remains under-studied in CSCW. In view of this, we discuss widening the ecology of care around family planning based on Bronfenbrenner's ecological theory.We also discuss the implications for designing family planning interventions that are mindful of women's needs.

Physiological signals hold immense potential for ubiquitous emotion monitoring, presenting numerous applications in emotion recognition. However, harnessing this potential is hindered by significant challenges, particularly in the collection of annotations that align with physiological changes since the process hinges heavily on human participants. In this work, we set out to study human participants' perspectives in the emotion data collection procedure. We conducted a lab-based emotion data collection study with 37 participants using 360° virtual reality video stimulus followed by semi-structured interviews with the study participants. Our findings presented that intrinsic factors like participants' perception, experiment design nuances, and experiment setup suitability impact their emotional response and annotation within lab settings. Drawing from our findings and prior research, we propose recommendations for incorporating participants' context into annotations and emphasizing participant-centric experiment designs. Furthermore, we explore current emotion data collection practices followed by AI practitioners and offer insights for future contributions leveraging physiological emotion data.

The shift away from multigenerational families to nuclear families in India has created a growing need to support older adults living independently. While technology can help address this gap, older adults' limited exposure to newer technology restricts the adoption of such solutions. However, they remain comfortable with long-standing technologies like television (TV). This study explores their daily technology usage and challenges, aiming to determine whether TV can be leveraged to improve their quality of life. We examined how TV systems could be enhanced to assist older adults with tasks such as staying connected, receiving health alerts, and ensuring security. Using a participatory design approach, we developed video probes using the prototype of the TV-based application and interviewed 27 older adults to assess its acceptance and usability. Our findings demonstrate older adults' strong interest in a TV-based solution and a preference for familiar technology to support security, independence, and wellbeing.

Despite global efforts for women's inclusion in the workforce, challenges persist, especially in conflict-affected areas where socio-political, economic, and cultural norms further impede their economic participation. We situate our work in Kashmir, India, a region with a long history of socio-political unrest. We conducted 15 semi-structured interviews with women from a non-government organization operating community training centers in Kashmir. These training centers have been established, focusing on skill development to encourage women to venture into entrepreneurship. Our findings reveal socio-political and cultural factors influence women's struggles. We emphasize the vital role of these centers and the role of technology in these centers. Finally, we offer socio-technical design recommendations to support women's skill-building and entrepreneurship.

COPD is an incurable Chronic Respiratory Disease that results in restricted airflow and respiratory issues. India faces rising pollution and health infrastructure challenges, significantly contributing to the prevalence of respiratory diseases like COPD. Studies have reported that COPD is India’s second leading cause of death and Disability Adjusted Life Years. Our study delves into the current state of COPD care and awareness in India. Our mixed-methods research, encompassing online surveys with medical personnel (n=9) and individuals (n=141) and semi-structured interviews (n=13) with various stakeholders (patients, doctors and caregivers), revealed a noteworthy lack of COPD awareness amongst the public, consequently affecting the COPD diagnosis, treatment and management strategies. We further explored how COPD affects patients’ self-perception and quality of life while identifying the barriers to COPD care. Finally, we conclude with design recommendations for technology-based interventions which can support the management of COPD patients in the Indian context.

Mental health well-being is a global concern, with disparities in treatment services being a challenge. Though, digital mental health interventions are proposed to bridge the gaps and supplement support and assistance. Yet, many individuals still struggle with mental health issues, particularly in regions encountering socio-political unrest, and face obstacles in seeking professional assistance. Situating our work in Kashmir, India, a region with a long history of socio-political unrest, we conducted 18 semi-structured interviews with participants seeking professional support to explore how individuals navigate mental health and professional help-seeking. Our findings identify the struggles in seeking support rooted in the context through socio-political and socio-cultural influences, strategies and methods adopted to navigate these struggles, and the role of technology in seeking support. Using a social-ecological approach to mental health care, we emphasize accounting for the socio-political realities that shape support-seeking in politically disturbed contexts and offer socio-technical design recommendations.

Traditional localization systems often rely on a network of external sensors, making the setups cumbersome, expensive, and requiring significant calibration effort. The advent of Bluetooth 5.1 and later versions brought enhancements that enable precise localization using constant tone extension (CTE) in the signal through Angle of Arrival (AoA) and Angle of Departure (AoD) techniques. This work examines the capacity of a single Bluetooth Low-Energy (BLE) locator with an antenna array based on AoA in terms of performance, efficiency, and latency in real-time indoor positioning. While traditional neural networks train measured entities to match calculated distances, we utilize the azimuth and elevation angle components in the AoA measured and train neural networks to match their theoretical counterparts. We conducted extensive experiments in a real-world lab environment, providing ablation studies in the design. The results demonstrate the system’s capability in real-time with many potential interference variables. Under lab conditions, our results show the capacity of a single locator wanes past 4m with the best average accuracy of 0.09m error in positioning within a 5m radius to as much as ∼ 1m of error beyond 6m up to the maximum possible measuring distance in the lab.

EEVR (Emotion Elicitation in Virtual Reality) is a novel dataset specifically designed for language supervision-based pre-training of emotion recognition tasks, such as valence and arousal classification. It features high-quality physiological signals, including electrodermal activity (EDA) and photoplethysmography (PPG), acquired through emotion elicitation via 360-degree virtual reality (VR) videos.Additionally, it includes subject-wise textual descriptions of emotions experienced during each stimulus gathered from qualitative interviews. The dataset consists of recordings from 37 participants and is the first dataset to pair raw text with physiological signals, providing additional contextual information that objective labels cannot offer. To leverage this dataset, we introduced the Contrastive Language Signal Pre-training (CLSP) method, which jointly learns representations using pairs of physiological signals and textual descriptions. Our results show that integrating self-reported textual descriptions with physiological signals significantly improves performance on emotion recognition tasks, such as arousal and valence classification. Moreover, our pre-trained CLSP model demonstrates strong zero-shot transferability to existing datasets, outperforming supervised baseline models, suggesting that the representations learned by our method are more contextualized and generalized. The dataset also includes baseline models for arousal, valence, and emotion classification, as well as code for data cleaning and feature extraction. Further details and access to the dataset are available at https://melangelabiiitd.github.io/EEVR/.

Rapid and widespread vaccination played a major role in stemming the flow of Covid-19 spread. In India, the government enabled free and accessible vaccination for Covid-19. Although the general public responded positively, pregnant and breastfeeding women in rural areas hesitated about COVID-19 vaccination, leading to low vaccine uptake. In this work, we started with a formative study to understand pregnant and breastfeeding women's vaccine hesitancy. We found that the main barriers were poor knowledge about vaccines and limited agency around vaccination. We deployed a chatbot over WhatsApp to tackle their hesitancy through evidence-based vaccine education. We studied the chatbot engagement of 800+ participants over 23 days through chat logs, surveys, and interviews. We present participants' experiences and aspirations around chatbots. We found the chatbot to work well under a constrained network to build participants' vaccine-related knowledge and intentions. We further discuss the role of chatbots in healthcare beyond COVID-19 vaccination.

The issue of youth unemployment is a global concern, with active initiatives, policies, and technologies in place to support employment seekers. Despite these efforts, youth unemployment in various regions is attributed to the complex challenges that impede access to resources, particularly in contexts characterized by socio-political unrest. To understand the use of job-seeking resources in such a context, we situate our work in the region of Jammu & Kashmir, which has a long-standing history of socio-political conflict ongoing for more than three decades. For this work, we conducted 21 semi-structured interviews with participants from Kashmir to understand their current job-seeking practices and the challenges they encounter during the entire process. Our findings present the systemic infrastructural challenges the youths face due to the ongoing crisis. Analyzing the youths' workarounds to mitigate these challenges, we discuss how we might leverage these workarounds into designing systems while navigating through the contextual complexities. We discuss the challenges of designing in the context and provide design recommendations to support job seekers.

In low-and middle-income countries like India, people with severe mental illness (PSMI) rely on their families as a primary source of care, given the lack of support from healthcare systems. The demanding nature of caregiving places significant physical and mental demands on caregivers, who are the primary source of support to PSMI. We explore how caregivers in under-resourced settings can be better supported through everyday digital technologies. We conducted interviews with caregivers (from urban and rural India), as well as workshops with professionals from Indian NGOs that work directly with PSMIs. We found that technology has the potential to (1) provide carer-centred support that empowers carers who experience stigma and issues with existing support networks; (2) provide support for carers to overcome barriers and progress in the recovery of the PSMI. We conclude with design considerations, proposing how an online peer community can leverage carers’ expertise to actualise support provision.

There has been a growing interest in reproductive health and intimate wellbeing in Human-Computer Interaction, increasingly from an ecological perspective. Much of this work is centered around women’s experiences across diverse settings, emphasizing men’s limited engagement and need for greater participation on these topics. Our research responds to this gap by investigating cisgender men’s experiences of cultivating sexual health literacies in an urban Indian context. We leverage media probes to stimulate focus group discussions, using popular media references on men’s fertility to elicit shared reflection. Our findings uncover the role that humor and masculinity play in shaping men’s perceptions of their sexual health and how this influences their sense of agency and participation in heterosexual intimate relationships. We further discuss how technologies might be designed to support men’s participation in these relationships as supportive partners and allies.

A recent surge in the application of Virtual Reality in education has made VR Learning Environments (VRLEs) prevalent in fields ranging from aviation, medicine, and skill training to teaching factual and conceptual content. In spite of multiple 3D affordances provided by VR, learning content placement in VRLEs has been mostly limited to a static placement in the environment. We conduct two studies to investigate the effect of different spatial representations of learning content in virtual environments on learning outcomes and user experience. In the first study, we studied the effects of placing content at four different places - world-anchored (TV screen placed in the environment), user-anchored (panel anchored to the wrist or head-mounted display of the user) and object-anchored (panel anchored to the object associated with current content) - in the VR environment with forty-two participants in the context of learning how to operate a laser cutting machine through an immersive tutorial. In the follow-up study, twenty-two participants from this study were given the option to choose from these four placements to understand their preferences. The effects of placements were examined on learning outcome measures - knowledge gain, knowledge transfer, cognitive load, user experience, and user preferences. We found that participants preferred user-anchored (controller condition) and object-anchored placement. While knowledge gain, knowledge transfer, and cognitive load were not found to be significantly different between the four conditions, the object-anchored placement scored significantly better than the TV screen and head-mounted display conditions on the user experience scales of attractiveness, stimulation, and novelty.

Menstrual tracking is a mechanism widely engaged towards preserving menstrual dignity, as natural birth control, for ensuring adequate preparation for an upcoming cycle, among other motivations. We investigate the design of digital menstrual trackers towards enabling period-positive ecologies in otherwise stigmatized contexts. We examine menstrual tracking practices across ages (12–65 yrs.) using a combination of methods—3 surveys (450+ responses), a cultural probe (10 adolescents), interviews (16 adults), and a review of (9) mobile applications. Our analysis highlights the diversity across menstrual tracking practices and the role of relationships in influencing these practices throughout the menstrual journey. We also identify menstrual tracking as an avenue towards the emancipation of those who menstruate. Finally, we draw on Martha Nussbaum’s central human capabilities to discuss sociotechnical implications for redesigning digital menstrual trackers towards crafting just and period-positive futures.

Sustainable Development Goal 4 promotes inclusive and equitable quality education and lifelong learning opportunities for all. However, regions with ongoing socio-political conflict suffer disruption to education and learning. We situate our work in Kashmir, India, affected by socio-political conflict for more than three decades. We did multiple field visits and conducted 21 semi-structured interviews with parents, teachers, students, and members of a non-government organization that runs Community Learning Centers in Kashmir. Our findings present the barriers in education caused by disruption and the role of community learning centers in overcoming the barriers within these contextual constraints. Further, we discuss engaging researchers and policymakers to leverage human infrastructure, embedding uncertainty into the design, infrastructuring trust, and content usability to develop solutions to make education more accessible. Despite significant research in HCI and Education, research in this particular context is under-explored, and our work contributes to filling this gap.

Limited interaction with professionals, infrastructural, and social constraints put barriers in providing holistic support to expectant and new mothers in low-resource settings. We examine the use of digital support groups facilitated through WhatsApp by a non-government organization in India. Complementing prior research, these digital peer support groups inform about an open public space created over a chat platform where rural communities and health professionals can engage. By qualitatively analyzing six months of interaction among 588 group members and collecting the experiences of the group moderators, we inform about how the support groups acted as an important source for compensating the gaps in the existing healthcare, providing reassurance support on routine health, explanation on test reports, validation and counseling support in ongoing treatments. We also derive implications for the future of digital support groups and the need for further research on the use of unplatformed design models in resource-constrained settings.

Women’s safety in public spaces has become a prime concern in India. Much of the academic and non-academic efforts have focused on developing technology-mediated solutions such as mobile apps and helplines for combating harassment experiences. However, there is an acute understanding of women’s perceptions and desires around these technological solutions. In this work, we investigate women’s perceptions around technological interventions dedicated to safety, the nature of support they seek from technology, and the characteristics they desire technology to embody to offer support. We draw on interviews with women across urban areas of India, demonstrating how they currently navigate unsafe experiences and also unpack their desires around technological solutions encompassing precautionary, reactionary, and healing support for navigating public spaces in the future. Finally, we conclude with implications focusing on the need for embedded technological solutions and going beyond victim-centric approaches to design technology for safety.

Safe menstrual hygiene practices are central to maintaining good menstrual health. In addition to menstrual literacy, practicing safe menstrual hygiene requires access to menstrual products and a private, safe, and hygienic space for changing and cleaning. Access to such infrastructure, specifically functional public toilets, becomes even more crucial for menstruators when navigating public spaces while having periods. This work presents the learnings from the prototyping of MenstruSPACE, a toilet locator application offering an annotation system for public toilets towards supporting menstrual mobilities. Our design journey revealed a set of design tensions and open challenges to be considered when designing public infrastructure annotation systems to facilitate safe menstrual mobilities.

Care workers are increasingly using digital technology in their daily lives, for monitoring, financial compensation, training, coordination, and more. State and corporate actors have invested significant resources to enable this digital shift, particularly during the COVID-19 pandemic. However, care work has remained chronically underpaid, and continues to rely on women from minoritized and marginalized backgrounds. Our paper examines how care workers carefully navigate digitization, precarity, and complex social relationships, in an attempt to care for their communities and each other. We analyze the emerging digital ecosystem for frontline health workers in India during the COVID-19 pandemic where these dynamics have been highly visible. Our research draws attention to four interconnected ways in which workers practiced care, by directing their efforts towards survival, resilience, advocacy, and/or resistance. We suggest these also as care orientations that can be adopted by researchers and practitioners, to critically reflect on and direct technology design towards enabling more caring futures, for (and with) workers and communities.

In India and other developing countries, Community Health Workers (CHWs) provide the first line of care in delivering necessary maternal and child health services. In this work, we assess the training and skill-building needs of CHWs, through a mobile-based training intervention deployed for six months to 500 CHWs for conducting 144 training sessions in rural India. We qualitatively probed 1178 questions, asked by CHWs, during training sessions, and conducted a content analysis of the learning material provided to CHWs. Further, we interviewed 48 CHWs to understand the rationale of information seeking and perceptions of training needs. We present our understanding of the knowledge gaps of CHWs and how the current learning material and training methods are ineffective in addressing it. Our study presents design implications for HCI4D researchers for mobile learning platforms targeted towards CHWs. We also provide policy-level suggestions to improve the training of CHWs in India or a similar context.

With easy access to affordable internet-powered smartphones, developing countries are adopting smartphone applications to provide enabling services to its citizens, through eHealth, eGovernance, and digital payments. The challenge is to ensure equitable access to these services by everyone, including people with semi-literacy or low-literacy who form a large part of the population in developing countries. However, extensive HCI literature has identified literacy as one of the barriers to designing user interfaces. In this work, we propose a framework of actionable guidelines for designing smartphone UIs that would be usable by low-literate users. We reviewed the last two decades of HCI literature engaging people with low literacy, to synthesize our framework-designing SARAL. To evaluate the framework, we conducted a preliminary study with a group of 20 practitioners and researchers working in the field of UI/UX/HCI. We also analyzed six publicly available industry reports on designing UIs for people with low-literacy. The proposed guidelines intend to support researchers, practitioners, designers, and implementers in the design and evaluation of UIs of smartphone applications for people with low literacy. We present the evolutionary nature of the proposed framework while highlighting the importance of adopting a translational approach when building such frameworks.

Metro trains have emerged as a sustainable and cost-effective solution against environmental concerns and productivity losses caused by road traffic congestion. Understanding commuters' needs and preferences are essential for promoting and providing metro services. In this work, we survey a group of Delhi metro commuters to study their metro convenience preferences. Using mixed-method analysis, we identify the factors that contribute to commuters' convenience through stated preference valuations obtained from a survey (368 valid responses). These factors are a combination of metro route attributes (boarding/de-boarding stations and line interchanges) shaped by commuter experiences associated with safety concerns, last-mile connectivity, and their perceptions of travel time, crowdedness, and getting a seat. Based on our findings, we discuss the implications these preferences and perceptions have on user experience with the Delhi metro. We then propose design guidelines to enhance the existing Delhi metro app to improve user experience with metro train service.

In cultural contexts where menstruation is a stigmatized health topic, daily management of menstrual hygiene comes with its set of challenges. Our research aims to identify and examine such challenges faced during menstruation in the urban environs of Delhi, India. Through participatory design activities and interviews conducted with 35 participants who identified as menstruating and female, and a survey with 139 responses, we investigate how participants deal with their periods on the go. We also examine participants' conceptualizations of safe spaces, where they are able to deal with their period on their own terms. Finally, we discuss how menstrual mobilities are being, and might be, supported through technology-based interventions for a third space, targeting the legibility, literacy, and legitimacy of surrounding environments.

Providing effective and efficient maternal and child healthcare is a challenge across the world. The challenge is even higher in developing countries, like India, where the doctor-to-patient ratio does not meet WHO prescribed numbers. Inadequate access to medical experts means that even simple queries about healthcare are not answered. In this work, we present our initial findings of using state-of-the-art Q&A systems, like IBM Watson, Rasa, and custom retrieval-based model, for answering simple queries related to maternal and child health. The query database, formed of short questions, is obtained from a real-world experiment where a medical expert answered the questions. The initial results are encouraging and indicate that further research in this area can help to answer a large number of queries thus reducing the work-load on a human medical expert.

Online health communities provide a means for parents to engage with each other and seek and offer support. The parents' conversations in online communities also provide a lens into the parenting practices, challenges, and what role technology can play. Previous studies discuss the engagement of parents on online platforms related to specific childcare concerns. To understand the experiences of parents with online health communities, during the initial years of parenting, we conducted a study on the BabyCenter platform. It is a web forum that provides information on different aspects of early childhood development and offers peer-to-peer interaction. In this paper, we investigate the patterns of users' engagement for early childhood queries. Based on qualitative analysis of publicly available data on BabyCenter, 390 posts from January 2019 to June 2019, we find that the users primarily construct their knowledge, and engage in social interactions on the platform. We also find limited multimedia usage for expressing health concerns. We discuss design implications for parenting forums that meet the multi-faceted needs of parents during the early years of parenting.

Appliance fault in buildings resulting in abnormal energy consumption is known as an anomaly. Traditionally, anomaly detection is performed either at aggregate, i.e., meter-level, or at appliance level. Meter-level anomaly detection does not identify the anomaly-causing appliance, while appliance-level detection requires submetering each appliance in the building. Non-Intrusive Load Monitoring (NILM) has been proposed as an alternative to submetering to detect when appliances are running as well as estimate the appliance energy consumption. So far, applications have revolved around meaningful energy feedback. In this paper, we assess whether NILM can indeed be used for anomaly detection, as an alternative to submetering. We propose a supervised anomaly detection approach, AEM, and evaluate the effectiveness of NILM for anomaly detection. The proposed approach first learns an appliance's normal operation and then monitors its energy consumption for anomaly detection. We resort to real data, aggregate and subme-tered data from the two-year long REFIT dataset. We explain why anomaly detection performs worse with NILM data as compared to submetered data, highlighting the need for new, anomaly-aware NILM approaches.

Mobile Crowdsensing leverages the sensing capabilities of multiple mobile devices to execute large-scale sensing tasks by breaking them into smaller tasks for execution on individual mobile devices. Task allocation algorithms are used to efficiently distribute these smaller sensing tasks to a subset of participants while optimizing system-level goals (such as location accuracy or data quality) for participant selection. The sensing tasks, e.g., collecting GPS tagged data, are often energy-intensive and battery consumption during sensing task execution remains a major concern for participants. So far no in-depth study exists that evaluates the impact of battery consumption on allocation algorithms. In this work, we conducted an in-depth study on the effects of battery consumption patterns of smartphone users. We studied the impact of battery consumption patterns extracted from a real-world data-set on standard as well as state-of-the-art algorithms to show how different battery usage patterns affect the performance of allocation algorithms. Our work provides an important insight into factors affecting the performance of allocation algorithms and advocates incorporating battery usage patterns for the future development of these algorithms.

Despite a crucial role in providing public health services, Community Health Workers (CHWs) remain disadvantaged in receiving effective skill-building opportunities. Due to the lack of health experts and appropriate infrastructure, it becomes challenging to provide training on a regular basis. Our aim is to investigate opportunities for designing technology-supported collaborative learning to compensate for the limited availability of instructors. We designed a mobile learning-based peer-led educational intervention, and conducted an eight week long between-group study with 120 CHWs across four districts of Delhi, India. We found that CHWs were able to participate and use the system on their own leading to significant knowledge gains and increased desire to learn. With little guidance, CHWs exhibited benefits of collaborative learning in terms of positive interdependence on each other and use of interpersonal skills. The informal peer learning environment encouraged CHWs to have discourses on deeper societal aspects e.g. their role in society.

Use of chatbots in different spheres of life is continuously increasing since a couple of years. We attempt to understand the potential of chatbots for breastfeeding education by conducting an Wizard-of-Oz experiment with 22 participants. Our participants included breastfeeding mothers and community health workers from the slum areas of Delhi, India. We prototyped our chatbot as an interactive question-answering application and analyzed users' interaction patterns, perceptions, and contexts of use. The chatbot use cases emerged primarily as the first line of support. The participants, especially the mothers, were enthusiastic with the opportunity to ask questions and get reliable answers. We also observed the influencing role of female relative, e.g. mothers-in-law, in breastfeeding practices. Our analysis of user information-seeking suggests that a majority of questions (88%) are of nature that can be answered by a chatbot application. We further observe that the queries are embedded deeply into myths and existing belief systems. Therefore requiring the designers to focus on subtle aspects for providing information such as positive reinforcement and contextual sensitivity. Further, we discuss, different societal and ethical issues associated with Chatbot usage for a public health topic such as breastfeeding education.

Cultural taboos and limiting social norms make it challenging to communicate and teach about menstrual health in India. We present findings from an inquiry of current approaches used to educate adolescents about menstruation, examining the perspectives of young adults, parents, teachers, social workers, and health professionals for identifying design opportunities and potential for impact. Our findings from the content analysis of education and training materials in use, an online survey of 391 adults, 52 interviews, and 2 focus groups indicate that although detailed and descriptive information materials are available for use, there exists a disconnect between parents’ and teachers’ expectations regarding who will introduce these topics to adolescents. We also highlight a clear difference in attitudes regarding who must be taught, how, where, and at what stages. Finally, we articulate factors that shape access and receptivity to this knowledge and engage with the lens of feminist HCI to discuss sociotechnical implications for the design of menstrual health education initiatives.

Voice forums are an effective intervention medium for marginalized communities to access information in a structured and localized manner. Users actively contribute by posting questions and responses in the form of audio messages, and thereby help in enriching the voice forum content. In order to build an audio library using the voice forums to disseminate information, significant manual effort is needed in analyzing and curating the data. This is one of the key impediments to the successful implementation of voice forums for knowledge dissemination and training.

Mobile CrowdSensing (MCS) applications rely on sensor data collected from a number of mobile participant devices; the participant devices need to sustain in the system for longer duration in order to services multiple requests. In this work, we propose two online load-balanced algorithms, that use available resources on mobile devices, to efficiently allocate tasks to a subset of participants. We have conducted extensive simulations to compare our algorithms with three baseline approaches and observed significant improvements in the system lifetime and the total number of tasks serviced. To further validate our results, we also conduct real-world experiments on 8 smartphones. We achieve 29.3% increase in the number of tasks serviced, with drastic improvements in system lifetime (in resource constrained cases) over the state-of-the-art approaches.

Menstruation has long remained a conversational taboo across India, resulting in inadequate dissemination of menstrual health education (MHE). Menstrupedia, a digital platform designed for an Indian audience, aims to bridge this information gap to impart MHE via its website and comic. We contribute a study of Menstrupedia---the information exchange on its website, the education it aims to provide, and the perceptions of its users. Using a combination of qualitative research methods, and engaging a feminist Human-Computer Interaction (HCI) lens, we critically analyze Menstrupedia's affordances and shortcomings. We also make recommendations for the design of technology-based dissemination of MHE, as well as additional sensitive and taboo topics.

The Healthcare system of India provides outreach services to the rural population with a key focus on the maternal and child health through its flagship program of Community Health Workers (CHWs). The program since its launch has reached a scale of over 900000 health workers across the country and observed significant benefits on the health indicators. However, traditional face to face training mechanisms face persistent challenge in providing adequate training and capacity building opportunities to CHWs which leads to their sub-optimal knowledge and skill sets. In this paper, we propose Sangoshthi, a low-cost mobile based training and learning platform that fits well into the environment of low-Internet access. Sangoshthi leverages the architecture that combines Internet and IVR technology to host real time training sessions with the CHWs having access to basic phones only. We present our findings of a four week long field deployment with 40 CHWs using both qualitative and quantitative methods. Sangoshthi offers a lively environment of peer learning that was well received by the CHW community and resulted into their knowledge gains (16%) and increased confidence levels to handle the cases. Our study highlights the potential of complementary training platforms that can empower CHWs in-situ without the need of additional infrastructure.

Interactive Voice Response (IVR) platforms have been widely deployed in resource-limited settings. These systems tend to afford asynchronous push interactions, and within the context of health, provide medication reminders, descriptions of symptoms and tips on self-management. Here, we present the development of an IVR system for resource-limited settings that enables real-time, synchronous interaction. Inspired by community radio, and calls for health systems that are truly local, we developed "Sehat ki Vaani". Sehat ki Vaani is a real-time IVR platform that enables hosting and participation in radio chat shows on community-led topics. We deployed Sehat ki Vaani with two communities in North India on topics related to the management of Type 2 diabetes and maternal health. Our deployments highlight the potential for synchronous IVR systems to offer community connection and localised sharing of experience, while also highlighting the complexity of producing, hosting and participating in radio shows in real time through IVR. We discuss the relative strengths and weaknesses of synchronous IVR systems, and highlight lessons learnt for interaction design in this area.

Waiting for the completion of a system process is an everyday experience. While waiting, system provides feedback to the user about ongoing process through temporal metaphors (Progress bar, Busy icons, etc.). One of the key performance requirement for temporal metaphors is to retain the user till the process completes. Researchers have evaluated these metaphors through subjective means, and objective assessment has not been well explored. In this paper, we present survival analysis as objective assessment method to evaluate temporal metaphors. Through a field experiment, we demonstrate the application of survival analysis and empirically establish that auditory progress bar (temporal metaphor for audio interfaces) works for callers of a distress helpline. To the best of our knowledge, it is the first study on distress callers. The paper further discusses the applicability of survival analysis for evaluating temporal metaphors and wait time experiments for other applications, tasks, and settings.

Developing region road traffic has a unique characteristic of high heterogeneity in vehicle types. In this paper, we describe VividhaVahana, a smartphone sensor based system to categorize road vehicles into four predominant categories: two-wheeler bikes, three-wheeler auto-rickshaws, four-wheeler car

Abundance of mobile phones in developing nations makes them an effective tool to spread information and communication technology. Currently different mobile based platforms are being used in a variety of ICT contexts in developing nations. These systems have been built with different features according to the context in which they are used. After studying existing tools, we have developed a generic mobile-based ICT tool, named MVoice, which can serve in multiple contexts with minimal configuration changes. Our tool can also be extended easily to satisfy new requirements. In this paper, we present our experience and results from four real-world deployments, each in a different context, of MVoice.

Social programs often require collection of demographic and program related information. Pen-paper surveys have been the most favorable way of collecting such information. However, with proliferation of smartphones, low cost mobile connectivity with good coverage and availability of several data collection applications that can even work around the connectivity concerns, pen-paper surveys are now being replaced by mobile based data collection. In this work, we discuss the enhancement of Open Data Kit (ODK), an existing mobile based data collection application, with features such as pre-filling and validation SMS. The additional features were motivated from the real world requirements of reducing the data collection effort and minimizing the discrepancies in records for a project requiring information dissemination to beneficiaries of National Rural Employment Guarantee Scheme (NREGS), a government of India scheme. Data collection exercise, using our extended tool, is currently ongoing in two districts across two different states in India. Preliminary results from one deployment indicate that even with 8% of pre-filled fields in a form, there was a 16.1% decrease in the errors. Additionally, time per survey reduced with the pre-filling option. Preliminary results from another deployment indicate that 35% of the pre-filled forms had mismatched or changed information when compared with the data available in government records.

Activist groups have taken up information dissemination and feedback collection as a means of rights advocacy in India. However, it is not easy given the difficulty in procuring and disseminating information at a large scale. Beneficiaries are often not able to help themselves as information systems are administration facing, because of poor literacy and the inability to access the Internet. Further, beneficiaries are not well informed of their rights and entitlements under different government schemes to know how and when to file grievances. We aim to solve these problems by designing and testing prototypes for information dissemination and feedback collection in various contexts. In our current prototype we describe an automated tool that sifts through the data on an MIS and conveys personalised information to the beneficiaries through voice calls. This is a work in progress, and our first exercise on providing MIS-extracted information to people through phone calls led to 70% of the beneficiaries who noticed a discrepancy in the data to agree to file a grievance on their behalf. We are continuing to scale the work, make it more automated, and run qualitative interviews with all stakeholders to understand causality linkages with transparency led grievance filing, assisted by appropriate ICTs, to increase accountability.

Increasing use of telephone devices has made the Interactive Voice Response (IVR), a technology for accessing information over phone, popular among the commercial organizations. IVR systems are used for critical applications like flight reservation, tele-banking, etc. which requires to have well tested IVR systems. Manual testing of an IVR application requires dialing number, listening and responding to voice prompts through key-press or speech.

Cardio-Vascular Diseases (CVD) are one of the major healthcare problems across the world, causing deaths for nearly 17 million people every year. With more than 80% of all CVD cases occurring in developing countries, it is a big challenge that needs immediate attention. Specifically for India, World Health Organization (WHO) and many other organizations have predicted rapid growth of CVD patients in near future. It is known that CVD can be prevented or deferred, if detected in its earlier stages and by subsequently adapting to appropriate preventive methods. Cost and availability of lab equipments -- for early diagnosis of CVD -- act as deterrents in controlling the spread of CVD cases in India, particularly in the rural parts.

Location-aware mobile applications are steadily gaining popularity across the world. However, lack of Global Positioning System (GPS) and absence of Wi-Fi infrastructure prevent users with non-Smart phones (majority of population in developing countries) from using location-aware applications as their phones do not have access to their current location. Existing GSM based approaches such as Cell ID-based works on non-Smart phones but they require access to a comprehensive database of Cell IDs. Such a database either does not exist or is very limited in developing countries. In this paper, we propose a novel GSM-based approach of using Cell Broadcast Service (CBS) messages for getting current location on the phone. Proposed approach does not depend on a comprehensive database and can run on programmable low end phones. We demonstrate the effectiveness of our approach on data collected in New Delhi, India across two different operators and propose two space-time history based algorithms to improve upon the localization accuracy of our baseline CBS approach. The proposed algorithms provide up to 35% improvement in accuracy over the baseline method. Further, we compare accuracy of our CBS-based approach with that of Cell ID-based approach and also, present a multimodal approach that uses combination of both CBS and Cell ID (wherever available) to improve the localization accuracy.

A computer-assisted language learning software for mobile devices (MAC) is presented, that was aimed to helping speakers acquire speech contrasts not native to their own language. The software is based on the high variability phonetic training (HVPT) technique. An overview of the software is given, followed by results from an efficacy study. Two groups using slightly different variations of the MAC software. One group received software training that was equivalent to an ordinary HVPT training, but delivered on mobile devices. The second group received a version that used an adaptive algorithm to determine which words and speakers that the learner had most difficulty with, and gave them more practice on those. Results showed that both groups showed significant but equivalent improvements. These results also showed that the magnitude of improvement using mobile phones was similar to those obtained using fixed PCs.

A key issue in mobile and ambient computing is the effort required to rapidly prototype and evaluate user interfaces and applications. Existing technologies for these tasks suffer either from low fidelity (e.g. paper prototypes, mental walkthroughs) or effectively require a near full-scale deployment. We propose an approach using immersive video with surround sound and a simulated infrastructure to create a very realistic environment in the office or the lab. It provides a low-cost and rapid means to prototype user interfaces and applications, and to evaluate them in a realistic simulation of the context, in which they are intended to be used.