I-Corps: A Smartphone-Human Hybrid System to Measure Human Behavior and Well-Being

这个I-Corps项目更广泛的影响/商业潜力集中在准确和全面收集人类行为和福祉数据，以改善决策和业务运营。该系统提供了一种收集人类行为和幸福数据的新方法。它有可能帮助政府机构设计更宜居的社区，城市和地区，以提高人类福祉。 该技术还可以帮助公司证明他们的产品和服务可以改善行为和健康结果。此外，智能手机拥有率的日益普及使得拟议的系统可以广泛用于大规模部署。这个I-Corps项目的特点是智能手机-人类混合智能系统，该系统结合了先进的数据挖掘和机器学习技术以及复杂的用户界面设计，允许智能手机和人类生成的数据之间进行交互。具体而言，智能手机传感器数据在手机上进行本地分析，并使用数据挖掘和机器学习算法进行真实的实时分析，以提取日常活动和旅行的客观时空细节。提取的信息减轻了答复者的负担，并作为初步信息基础，以附上关于日常活动和旅行的更详细和准确的信息。来自人类用户的信息反过来优化了用于分析智能手机传感器数据的数据挖掘和机器学习算法，并随着时间的推移提高了智能手机生成数据的准确性。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project centers on accurate and comprehensive collection of human behavioral and well-being data for improved policymaking and business operations. The proposed system provides a new way to collect human behavior and well-being data. It has the potential to help government agencies design more livable neighborhoods, cities, and regions for greater human well-being. The technology may also help companies demonstrate that their products and services improve behavioral and well-being outcomes. In addition, the increasing prevalence of smartphone ownership makes the proposed system widely accessible for large-scale deployment. This I-Corps project features a smartphone-human hybrid intelligence system that incorporates advanced data mining and machine learning techniques and sophisticated user interface design to allow interactions between smartphone- and human-generated data. Specifically, smartphone sensor data are analyzed locally on the phone and in real time using data mining and machine learning algorithms to extract objective, spatiotemporal details of daily activities and trips. The extracted information reduces respondent burden and serves as an initial information basis to attach more detailed and accurate information on daily activities and trips. The information from the human user, in turn, optimizes the data mining and machine learning algorithms for analyzing smartphone sensor data and increases accuracy in smartphone-generated data over time. The result is a novel system in which smartphone sensing collaborates with human input to generate accurate and comprehensive data related to human behavior and well-being.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.