  This study tackles on a new problem of estimating human-error potential on a
shop floor on the basis of wearable sensors. Unlike existing studies that
utilize biometric sensing technology to estimate people's internal state such
as fatigue and mental stress, we attempt to estimate the human-error potential
in a situation where a target person does not stay calm, which is much more
difficult as sensor noise significantly increases. We propose a novel
formulation, in which the human-error-potential estimation problem is reduced
to a classification problem, and introduce a new method that can be used for
solving the classification problem even with noisy sensing data. The key ideas
are to model the process of calculating biometric indices probabilistically so
that the prior knowledge on the biometric indices can be integrated, and to
utilize the features that represent the movement of target persons in
combination with biometric features. The experimental analysis showed that our
method effectively estimates the human-error potential.
