  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.
