Stress remains a significant health concern, yet commercial monitoring devices are often too expensive and complex for populations in developing countries. This research presents Spondon, a low-cost, multi-sensor wearable designed to detect stress in resource-constrained environments. Utilizing an ESP32 microcontroller with MAX30102, GSR, and temperature sensors, the device collects physiological data. We evaluated the system with 15 participants using the Stroop test to induce stress across baseline, task, and recovery phases. Statistical analysis confirmed a distinct 7.95% drop in HRV (RMSSD) during stress. The system achieved a 75.0% accuracy in binary acute stress detection (Baseline vs. Stress) using a Random Forest classifier with personalized baseline normalization. In addition, when evaluating continuous real-world monitoring across three physiological states (Baseline, Stress, and Recovery), Spondon achieved a 58.4% multi-class accuracy, successfully outperforming the 33.3% random-chance baseline. This work contributes a validated, accessible hardware platform and methodology, democratizing stress research for low-income communities and highlighting the importance of personalized, context-aware design in HCI.

System level support is required for modern day Internet of Things (IoT) based applications. It opens up opportunities for low-cost solution approaches. We have developed a system solution for real time IoT based applications that allows real time access to location information and allows level of data abstraction for tracing location to be used in emergency situations. We have developed a wearable IoT solution to deal with the problem of sexual harassment on top of the system named Jalli. The application considers a developing country context. We hope that a system that is aware and sensitive about the resource limitations can bring out best possible system performance in the context of low resource regions.

Due to usage of self-reported data which may contain biasness, the existing studies may not unveil the exact relation between academic grades and app categories such as Video. Additionally, the existing systems' requirement for data of prolonged period to predict grades may not facilitate early intervention to improve it. Thus, we presented an app that retrieves past 7 days' actual app usage data within a second (Mean=0.31s, SD=1.1s). Our analysis on 124 Bangladeshi students' real-time data demonstrates app usage sessions have a significant (p<0.05) negative association with CGPA. However, the Productivity and Books categories have a significant positive association whereas Video has a significant negative association. Moreover, the high and low CGPA holders have significantly different app usage behavior. Leveraging only the instantly accessed data, our machine learning model predicts CGPA within ±0.36 of the actual CGPA. We discuss the design implications that can be potential for students to improve grades.

Indigenous communities in Bangladesh are comparatively disadvantaged and face several barriers regarding rights. Access to technology and ICT can help indigenous communities open new economic, political, and social dimensions. The recent COVID-19 pandemic necessitated technology adoption for routine use, which is equally important for indigenous communities, but their technology adoption scenario remains unexplored in HCI research. Considering the research gap, we interviewed n=36 (Female 26 and Male 10) indigenous people from six different indigenous communities in Chattogarm and Sylhet divisions in Bangladesh. We found that they are strongly connected in communities, have independent technology access, and have no gender differences. They have a strong interest and eagerness to learn available technologies that help them in their professions, enrich their technical skills, communication, social participation, and expand the business. The study also revealed some challenges while using technology, but that did not negatively impact their usage. The study also discussed the community-centric strengths that helped them fight against the COVID-19 crisis and work for their development. This research impacts HCI literature, revealing the technology adoption scenarios of Indigenous communities in Bangladesh.