论文标题
使用生理传感器检测知识工作者的情感流量状态
Detecting Affective Flow States of Knowledge Workers Using Physiological Sensors
论文作者
论文摘要
工作中的流动经验对于生产力和工人福祉至关重要。但是，很难客观地检测工人何时在工作中流动。在本文中，我们研究了如何基于生理信号来预测工人的重点状态。我们进行了一项实验室研究，以收集知识工作者的生理数据，同时执行工作任务时经历了不同水平的流程。我们使用流量的九种特征来设计将引起不同焦点状态的任务。使用流量短规模检查的操作检查参与者经历了三种不同的流量状态，一种过于挑战的非流量状态以及两种类型的流量状态，平衡流量和自动流动。我们构建了机器学习分类器，可以区分为0.889平均AUC的非流量状态和流量状态，并从平均AUC的工作状态下进行静止状态。结果表明，生理感知可以检测知识工作者的集中流动状态，并可以使个人和组织提高生产力和工人满意度。
Flow-like experiences at work are important for productivity and worker well-being. However, it is difficult to objectively detect when workers are experiencing flow in their work. In this paper, we investigate how to predict a worker's focus state based on physiological signals. We conducted a lab study to collect physiological data from knowledge workers experienced different levels of flow while performing work tasks. We used the nine characteristics of flow to design tasks that would induce different focus states. A manipulation check using the Flow Short Scale verified that participants experienced three distinct flow states, one overly challenging non-flow state, and two types of flow states, balanced flow, and automatic flow. We built machine learning classifiers that can distinguish between non-flow and flow states with 0.889 average AUC and rest states from working states with 0.98 average AUC. The results show that physiological sensing can detect focused flow states of knowledge workers and can enable ways to for individuals and organizations to improve both productivity and worker satisfaction.
