Those working in Engineering for Global Development seek to improve the conditions in developing countries. A common metric for understanding the development state of a given country is the Human Development Index (HDI), which focuses on three dimensions: health, education, and income. An engineer’s expertise does not always align with any of those dimensions directly, while they still hope to perform impactful work for human development. To discover other areas of expertise that are highly associated with the HDI, correlations and variable selection were performed between all World Development Indicators and the HDI. The resultant associations are presented according to industry sector for a straightforward connection to engineering expertise. The associated areas of expertise can be used during opportunity development as surrogates for focusing on the HDI dimensions themselves. The data analysis shows that work related to "Trade, Transportation, and Utilities", such as electricity distribution, and exports or imports, "Natural Resources and Mining", such as energy resources, agriculture or access to clean water, and "Manufacturing", in general, are most commonly associated with improvements in the HDI in developing countries. Also, because the associations were discovered at country-level, they direct where geographically particular areas of expertise have been historically associated with improving HDI.

This dissertation collects principles and insights from various sources related to design for the developing world. These principles and insights form part of the foundation that can guide other engineers working in this area. The sources are the published literature, practitioners, non-governmental organizations, and our own field studies. From the engineering literature, we identified nine principles to guide engineers as they design poverty alleviating products for developing communities. Each principle is articulated, supporting literature is described, an in-depth example from the literature is given, followed by suggestions for how the principle can be applied to day-to-day engineering activities. Next, the work from engineering practitioners is studied. Information from various field reports was analyzed, a list of seven common pitfalls was derived, and the Design for the Developing World Canvas is introduced. This tool is similar to a Business Model Canvas, but it focuses on the product development process rather than the development of a business model. The Design for the Developing World Canvas can be used by design teams to facilitate discussions and make decisions that will allowthem to avoid the common pitfalls identified. A case study is then shared from a non-governmental organization called WHOlives.org about their experience with the Village Drill, a human-powered machine that digs boreholes for water wells. The case study outlines the development of the drill, a timeline of its implementation in 15 countries across three continents, specific values related to cashflows of the organization, and a conservative estimate of their impact in developing communities. A study of our original research conducting field studies using a technique called ethnography is then shared. This study was conducted in four countries on four continents and shows the impact of various conditions on the ability of the design team to collect information that is useful for making product development decisions. The conditions in this study include cultural familiarity, language fluency, gender and age of the respondent, information source type, use of prototypes, and others. The results can guide design teams as they make decisions about who to include on the design team, which projects to pursue, and how to conduct their own field studies. Lastly, conclusions related to design for the developing world are made based on the work presented and potential areas of future work are outlined.

Evaluating the social impacts of engineered products, or effects products have on the daily lives of individuals, is critical to ensuring that products are having positive impacts while avoiding negative impacts and to learning how to improve product designs for a more positive social impact. One approach to quantifying a product's social impact is to use social impact indicators that combine user data in a meaningful way to give insight into the current social condition of an individual or population. However, determining social impact indicators relative to engineered products and individuals in developing countries can be difficult when there is a large geographical distance between the users of a product and those designing them and since many conventional methods of user data collection require direct human interaction with or observation of users of a product. This means user data may only be collected at a single instance in time and infrequently due to the large human resources and cost associated with obtaining them. Alternatively, internet-connected, remote data collection devices paired with deep learning models can provide an effective way to use in-situ sensors to collect data required to calculate social impact indicators remotely, continuously, and less expensively than other methods. This research has identified key principles that can enable researchers, designers, and practitioners to avoid pitfalls and challenges that could be encountered at various stages of the process of using remote sensor devices and deep learning to evaluate social impact indicators of products in developing countries. Chapter 2 introduces a framework that outlines how low-fidelity user data often obtainable using remote sensors or digital technology can be collected and correlated with high-fidelity, infrequently collected user data to enable continuous, remote monitoring of engineered products using deep learning. An example application of this framework demonstrates how it can be used to collect data for calculating several social impact indicators related to water hand pumps in Uganda during a 4 day study. Chapter 3 builds on the framework established in Chapter 2 to provide principles for enabling insights when engaging in long-term deployment of using in-situ sensors and deep learning to monitor the social impact indicators of products in developing countries. These principles were identified while using this approach to monitor the social impact indicators of a water hand pump in Uganda over a 5 month data collection period. Chapter 4 provides principles for successfully developing remote data collection devices used to collect user data for determining social impact indicators. A design tool called the "Social Impact Sensor Canvas" is provided to guide device development along with a discussion of the key decisions, critical questions, common options, and considerations that should be addressed during each stage of device development to increase the likelihood of success. Lastly, Chapter 5 discusses the conclusions made possible through this research along with proposed future work.