Our research aims to support community health workers (CHWs) in low-resource settings by providing them with personalized information regarding their work. This information is delivered through a combination of voice- and web-based feedback that is derived from data already collected by CHWs. We describe the in situ participatory design approach used to create usable and appropriate feedback for low-literate CHWs and present usage data from a 12-month study with 71 CHWs in India. We show how the system supported and motivated CHWs, and how they used both the web- and voice-based systems, and each of the visualizations, for different reasons. We also show that the comparative feedback provided by the system introduced elements of competition that discouraged some CHWs while motivating others. Taken together, our findings suggest that providing personalized voice- and web-based feedback could be an effective way to support and motivate CHWs in low-resource settings.

Accredited Social Health Activists (ASHAs) have been shown to have a positive impact on health outcomes of the households they visit, particularly in maternal and neonatal health. As the first line of the public health system in many countries, they are a critical link to the broader public health infrastructure for community members. Yet they do this all with minimal training and limited support infrastructure. To a pregnant woman, an ASHA is a trusted ally in navigating the health system---information gathered is returned by appropriate advice and counseling. To the health system, the ASHA is a key channel of valuable householdlevel information for the public health system, yet she generally receives minimal guidance in return. In this paper we present ASTA---the ASHA Self-Tracking Application---a system that provides ASHAs with timely, on-demand information regarding their own performance compared to their peers. Using ASTA, ASHAs access comparative performance data through both a web-based and voice-based interface on demand. We evaluated ASTA through a 12-month deployment with 142 ASHAs in Uttar Pradesh, India, assessing the impact of providing feedback on ASHA performance. We find that ASHAs with access to the ASTA system made significantly more client visits, with average monthly visits 21.5% higher than ASHAs who had access to a control system. In addition, higher ASHA performance was correlated with increased usage of ASTA. However, the performance improvement was front-loaded, with the impact of the system decreasing toward the end of the study period. Taken together, our findings provide promising evidence that studying and incorporating tools like ASTA could be cost effective and impactful for ASHA programs.

Community health workers (CHWs) have been shown to be an effective and powerful intervention for improving community health. Routine visits, for example, can lower maternal and neonatal mortality rates. Despite these benefits, many challenges, including supervision and support, make CHW programs difficult to maintain. An increasing number of mHealth projects are providing CHWs with mobile phones to support their work, which opens up opportunities for real-time supervision of the program. Taking advantage of this potential, we evaluated the impact of SMS reminders to improve the promptness of routine CHW visits, first in a pilot study in Dodoma, Tanzania, followed by two larger studies with 87 CHWs in Dar es Salaam, Tanzania. The first Dar es Salaam study evaluated an escalating reminder system that sent SMS reminders directly to the CHW before notifying the CHW's supervisor after several overdue days. The reminders resulted in an 86% reduction in the average number of days a CHW's clients were overdue (9.7 to 1.4 days), with only a small number of cases ever escalating to the supervisor. However, when the step of escalating to the supervisor was removed in the second study, CHW performance significantly decreased.

Every year almost 10 million children die before reaching the age of five despite the fact that two-thirds of these deaths could be prevented by effective low-cost interventions. To combat this, the World Health Organization (WHO) and UNICEF developed the Integrated Management of Childhood Illness (IMCI) treatment algorithms.

Summarizing large multidimensional datasets is a challenging task, often requiring extensive investigation by a user to identify overall trends and important exceptions to them. While many visualization tools help a user produce a single summary of the data at a time, they require the user to explore the dataset manually. Our idea is to have the computer perform an exhaustive search and inform the user about where further investigation is warranted. Our algorithm takes a large, multidimensional dataset as input, along with a specification of the user's goals, and produces a concise summary that can be clearly visualized in bar graphs or linegraphs. We demonstrate our techniques in a sample prototype for summarizing information stored in spreadsheet databases.

This paper reports on a study of human subjects with a robot designed to mimic human conversational gaze behavior in collaborative conversation. The robot and the human subject together performed a demonstration of an invention created at our laboratory; the demonstration lasted 3 to 3.5 minutes. We briefly discuss the robot architecture and then focus the paper on a study of the effects of the robot operating in two different conditions. We offer some conclusions based on the study about the importance of engagement for 3D IUIs. We will present video clips of the subject interactions with the robot at the conference.

We present new lowest energy configurations for several large benchmark problems for the two-dimensional hydrophobic-hydrophilic model. We found these solutions with a generic implementation of tabu search using an apparently novel set of transformations that we call pull moves. Our experiments show that our algorithm can find these best solutions in 3 to 14 hours, on average. Pull moves appear quite effective and may also be useful for other local search algorithms for the problem. Additionally, we prove that pull moves are complete; that is, any pair of valid configurations are mutually reachable through a sequence of pull moves. Our implementation was developed with the Human-Guided Search (HuGS) middleware, which allows rapid development of interactive optimization systems.

In this paper we develop a generalized approach to visualizing and controlling an optimization process. Our framework, called Human-Guided Search, actively involves people in the process of optimization. We provide simple and general visual metaphors that allow users to focus and constrain the exploration of the search space. We demonstrate that these metaphors apply to a wide variety of problems and optimization algorithms. Our software toolkit supports rapid development of human-guided search systems.

The Personal Digital Historian (PDH) is an ongoing research project aimed at allowing small groups of people co-present to casually browse, embellish, and explore large collections of their personal data, such as pictures, video, or more business-related items such as spreadsheets or PowerPoint slides. In this interactive poster, we demonstrate our initial prototype system which is designed for a tabletop display. The interface allows people to organize their images along the four questions essential to storytelling: who?, when?, where?, and what? Users are provided with a wide variety of flexible interaction methods, including region of interest query specification with in-place freeform stroke input, image-based book marking, suggestion generation via automatic query relaxation, and output summarization. With this interface, the users can enjoy their conversation while having the photos at their fingertips, rather than being distracted by the effort of formulating queries.

This paper presents visualization and layout schemes developed for a novel circular user interface designed for a round, tabletop display. Since all the displayed items are in a polar coordinate system, many interface and visualization schemes must be revisited to account for this new layout of UI elements. We discuss the direct implications of such a circular interface on document orientation. We describe two types of fisheye deformation of the circular layout and explain how to use them in a multi-person collaborative interface. These two schemes provide a general layout framework for circular interfaces. We have also designed a new visualization technique derived from the particularities of the circular layout we have highlighted. In this technique the user controls the layout of the elements of a hierarchical tree. Our approach is to provide the user rich interaction possibilities to easily and quickly produce a layout comparable to the hyperbolic view developed at Xerox PARC. The visualization work presented in this paper is part of our ongoing Personal Digital Historian (PDH) research project. The overall goal of PDH is to investigate ways to effectively and intuitively organize, navigate, browse, present and visualize digital data in an interactive multi-person conversational setting.

Scheduling, routing, and layout tasks are examples of hard optimization problems with broad application in industry. Past research in this area has focused on algorithmic issues. However, this approach neglects many important human-computer interaction issues that must be addressed to provide people with practical solutions to optimization problems. Automatic methods do not leverage human expertise and can only find solutions that are optimal with regard to an invariably over-simplified problem description. Furthermore, users must understand the generated solutions in order to implement, justify, or modify them. Interactive optimization helps address these issues but has not previously been studied in detail. This paper describes experiments on an interactive optimization system that explore the most appropriate way to combine the respective strengths of people and computers. Our results show that users can successfully identify promising areas of the search space as well as manage the amount of computational effort expended on different subproblems

Organizations, families, institutions evolve a shared culture and history. In this work, we describe a system to facilitate conversation and storytelling about this collective past. Users explore digital archives of shared materials such as photographs, video, and text documents on a tabletop interface. Both the software and the interface encourage natural conversation and reflection. This work is an application of our ongoing research on systems for multiple, co-present users to explore digital collections. In this paper, we present a case study of our own group history along with the software extensions developed for this scenario. These extensions include methods for easily branching off from and returning to previous threads of the exploration, incorporating background contexts that support a variety of view points and flexible story sharing, and supporting the active and passive discovery of relevant information.

We present visualization and layout algorithms that can enhance informal story-telling using personal digital data such as photos in a face-to-face social setting. In order to build a more intuitive browser for retrieval, navigation and story-telling, we introduce a novel optimized layout technique for large image sets, which respects (context-sensitive) mutual similarities as visualized on a shared 2-D display (a table-top). The experimental results show a more perceptually intuitive and informative visualization of traditional CBIR-based retrievals, providing not only a better understanding of the query context but also aiding the user informing new queries. A framework for user-modeling is also introduced and tested. This allows the system to adapt to the user's preference and integrate relevance feedback.

Computer tutors and intelligent software assistants have traditionally been thought of as different kinds of systems. However tutors and assistants share many properties. We have incorporated tutorial strategies into an intelligent assistant based on the COLLAGEN architecture. We are working on an agent, named Triton, which teaches and helps users with the graphical user interface of an air travel planning system. We found that the collaborative model underlying COLLAGEN is an excellent foundation for both an assistant and a tutor, and that both modes of interaction can be implemented in the same system with different parameter settings.

Classification algorithms are difficult to apply to sequential examples because there is a vast number of potentially useful features for describing each example.Past work on feature selection has focused on searching the space of all subsets of features, which is intractable for large feature sets.We adapt sequence mining techniques to aEi as a preprocessor to select features for standard classification algorithms such as Naive Bayes and Winnow.Our experiments on three different datasets show that the features produced by our algorithm improve classification accuracy by lo-50%,