Algorithms for Quantifying Human Behavioral Dynamics from Smartphone Sensor Data

计算机和计算机系统并非独立于人类行为。我们所处的位置和我们所做的事情决定了物理和虚拟基础设施的位置和算法。这些行为的影响从计算机科学延伸到土木工程和建筑（我们在哪里，我们如何在空间中移动？），公共卫生领域（我们接触了多久，与谁接触？），甚至社会科学（如何通过空间调节流动性？）。这些行为中的许多都与我们在哪里、和谁在一起以及我们在做什么有关。了解人们在哪里，他们和谁在一起，可以帮助建模者更好地了解空间的使用或疾病的传播，无线网络设计师希望提供更好的响应更快的服务，甚至社交机器人也可以了解他们何时何地可能遇到特定的人。直到最近，收集这些数据一直受到技术的阻碍。社会科学家的传统工具要么过于生硬（如调查或人口普查数据），要么过于昂贵而无法广泛应用（如直接观察或民族志）。传感器小型化的最新进展，以及它们与智能手机的结合，使得收集有关人类行为的地点、时间、方式和与谁在一起的高保真数据成为可能。在过去的四年里，我们的实验室在设计和部署这类系统方面取得了相当大的成功。然而，收集数据只是冰山一角。千兆字节的行为数据需要以这样一种方式处理，即允许研究人员以导致洞察力的方式操纵数据。这项拨款旨在研究我们如何以合理的方式对人类行为进行编码。就像湍流水流的一般行为可以用一两个简单的比率来概括一样，我们希望推导出人类行为的度量，以及计算它们的有效计算算法。这项研究将产生深远而深远的影响，改变有关公共卫生和基础设施的决策方式，并提供一种新的方式来理解人类的样子

Computers and computer systems are not independent of human behaviour. Where we are and what we do drives placement and algorithms in both physical and virtual infrastructure. The impact of these behaviours extend beyond computer science to civil engineering and architecture (where we are and how we move through space?), public health fields (for how long and with whom have we been in contact?), and even the social sciences (how is mobility mediated by space?). Many of these behaviours are anchored in where we are, who we are with, and what we are doing. Understanding where people are and who they are with helps modelers who seek to better understand the use of space or the spread of disease, wireless network designers looking to provide better more responsive service, and even social robots understand where and when they might meet specific individuals. ***Until recently, gathering this data has been hampered by technology. The traditional tools of the social scientist are either too blunt (e.g. surveys or census data) or too expensive to deploy widely (e.g. direct observation or ethnographies). Recent advances in the miniaturization of sensors, and their incorporation into smartphones, has made collecting high fidelity data on the where, when, how and with whom of human behaviour automatable. Our lab has had considerable success over the last four years designing and deploying these kinds of systems.***However, collecting data is just the tip of the iceberg. Gigabytes of behavioural data need to be processed in such a way as to allow researchers to manipulate the data in ways that lead to insight. This grant proposes to investigate how we might encode the behaviour of humans in reasonable ways. Just as the general behaviour of a turbulent water flow can be encapsulated in one or two simple ratios, we hope to derive metrics of human behaviour, and efficient computational algorithms for computing them.***This research will have profound and far-reaching impacts, changing the way that decisions about public health and infrastructure are made, and providing a new way of understanding what it looks like to be human.**