Bangladesh (a low-income country) has a significant number of people dependent on alms for daily survival. These people, who we address as extremely impoverished people (EIP) are deprived of even basic healthcare. Their extreme levels of poverty, coupled with low literacy skills, and complete lack of access to technology means that they are unaware of existing low-cost/free healthcare services (as arranged by local hospitals) available for EIPs. In this paper, we address this gap by means of a carefully-crafted solution, Dakter Bari (a term in Bengali that translates to "Doctor's Home'' in English), that is contextually tailored to enable healthcare access to impoverished people. Extracting critical insights from our field study with (N=70) EIPs, we create a pathway for availing lower-cost healthcare solutions using intermediaries for information dissemination. These intermediaries are small businesses that impoverished people visit often. We also conduct field studies with (N=71) intermediary partners and (N=10) hospitals to identify challenges and realities of such intermediary-based solutions. Based on our findings, we design, iteratively develop, deploy, and user-test our system in real cases and collect feedback from related stakeholders. Preliminary analysis on usage of our system (deployed at intermediaries) revealed 255 healthcare requests made by EIPs via our system in six months. We connect our finding to the broader interests of CSCW around contextualized intermediation, inclusive healthcare, and sustainability of deployed systems.

Most scholarly discussions around ridesharing applications center on the experiences of the drivers and the riders (passengers), and thus the role of the owners of the cars, if they are different from the drivers, remain understudied. However, in many countries in the Global South, the car owners are often different from the car drivers, and the tensions between them often shape the experience with these ridesharing apps in those countries. In this paper, we address this issue based on our interview-based study in Dhaka, Bangladesh, which incorporates semi-structured interviews of 31 Uber drivers and 10 car owners. From our interviews, we identify the contract models that facilitate the partnership between prospective Uber drivers without a car and car owners seeking to rent their cars for Uber, describe the tensions between these two parties, provide a nuanced cultural portrayal of their negotiation mechanisms, and highlight the reasons for which the driver or the owner leaves Uber. Our analysis reveals how the local adoption of sharing economy amplifies existing inequalities and disrupts the prevailing social dynamics. We also connect our findings to the broader interests of CSCW around work, privacy, power and discuss their implications for design and policy formulations.

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Identifying physical traits and emotions based on system-sensed physical activities is a challenging problem in the realm of humancomputer interaction. Our work contributes in this context by investigating an underlying connection between head movements and corresponding traits and emotions. To do so, we utilize a head movement measuring device called eSense, which gives acceleration and rotation of a head. Here, first, we conduct a thorough study over head movement data collected from 46 persons using eSense while inducing five different emotional states over them in isolation. Our analysis reveals several new head movement based findings, which in turn, leads us to a novel unified solution for identifying different human traits and emotions through exploiting machine learning techniques over head movement data. Our analysis confirms that the proposed solution can result in high accuracy over the collected data. Accordingly, we develop an integrated unified solution for real-time emotion and trait identification using head movement data leveraging outcomes of our analysis. CCS CONCEPTS• Human-centered computing Interactive systems and tools.

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