Combining Direct and Indirect User Data for Calculating Social Impact Indicators of Products in Developing Countries
Combining Direct and Indirect User Data for Calculating Social Impact Indicators of Products in Developing Countries
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DOI：
10.1115/1.4047433
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发表时间：
2020-12
影响因子：
3.3
通讯作者：
Bryan J. Stringham;Daniel O. Smith;C. Mattson;E. Dahlin
中科院分区：
文献类型：
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作者：
Bryan J. Stringham;Daniel O. Smith;C. Mattson;E. Dahlin
评估工程产品的社会影响对于确保产品产生预期的积极影响以及学习如何改进产品设计以产生更积极的社会影响至关重要。通过使用社会影响指标可以对产品的社会影响进行定量评估，该指标以有意义的方式结合用户数据，以深入了解个人或群体当前的社会状况。大多数现有的收集社会影响指标用户数据的方法都需要与产品用户进行直接的人际互动（例如访谈、调查和观察研究）。这些交互产生高保真数据，有助于指示产品影响，但仅限于及时的单个快照，并且由于与获取这些数据相关的大量人力资源和成本，通常很少收集。本文提出了一个框架，概述了如何收集通常使用远程传感器、卫星或数字技术获得的低保真数据，并将其与高保真、不常收集的数据关联起来，以便通过用户数据对工程产品进行连续、远程监控。这些用户数据对于确定可用于事后社会影响评估的当前社会影响指标至关重要。我们通过演示如何使用该框架收集数据来计算与乌干达手动水泵相关的几个社会影响指标来说明该框架的应用。此示例的关键是使用深度学习模型将用户类型（男性、女性或成年儿童）与通过集成运动单元传感器获得的 1200 名手动泵用户的原始手动泵数据关联起来。
Evaluating the social impacts of engineered products is critical to ensuring that products are having their intended positive impacts and learning how to improve product designs for a more positive social impact. Quantitative evaluation of product social impacts is made possible through the use of social impact indicators, which combine the user data in a meaningful way to give insight into the current social condition of an individual or population. Most existing methods for collecting these user data for social impact indicators require direct human interaction with users of a product (e.g., interviews, surveys, and observational studies). These interactions produce high-fidelity data that help indicate the product impact but only at a single snapshot in time and are typically infrequently collected due to the large human resources and cost associated with obtaining them. In this article, a framework is proposed that outlines how low-fidelity data often obtainable using remote sensors, satellites, or digital technology can be collected and correlated with high-fidelity, infrequently collected data to enable continuous, remote monitoring of engineered products via the user data. These user data are critical to determining current social impact indicators that can be used in a posteriori social impact evaluation. We illustrate an application of this framework by demonstrating how it can be used to collect data for calculating several social impact indicators related to water hand pumps in Uganda. Key to this example is the use of a deep learning model to correlate user type (man, woman, or child statured) with the raw hand pump data obtained via an integrated motion unit sensor for 1200 hand pump users.