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Abstract: When humans
interact with systems, we are limited to the direct commands to the system and results
from these commands. While a machine is discrete and deterministic, the human is rich
and unpredictable and may give off visible or physiological signs as to his or her
current state. However, the system cannot detect these cues, thus leaving this fertile
source of input untapped. ... read morePhysiological sensing can remove this limitation by allowing a
system to know more about a user's current state and react accordingly. In this thesis,
I use functional near-infrared spectroscopy (fNIRS), a non-invasive brain-sensing
technology, to determine user state with no additional effort from the user and adapt
intelligent systems in real time. I present work showing that we can improve user
performance by modifying the goals for a user or by modifying the user interface. I
discuss design principles and present a taxonomy of adaptive strategies to best utilize
this input. This research aims to make brain-sensing more practical and efficient for
adaptive systems.
Thesis (Ph.D.)--Tufts University,
2015.
Submitted to the Dept. of Computer
Science.
Advisor: Robert
Jacob.
Committee: Remco Chang, Sergio Fantini, Bryan
Reimer, and R. Benjamin Shapiro.
Keyword: Computer
science.read less
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