Human-error-potential Estimation based on Wearable Biometric Sensors

TL;DR

This paper presents a novel approach to estimate human-error potential on shop floors using wearable biometric sensors, addressing challenges posed by sensor noise and dynamic human states.

Contribution

It introduces a probabilistic modeling method that combines biometric indices and movement features to classify human-error potential despite noisy sensor data.

Findings

Effective estimation of human-error potential demonstrated

Probabilistic modeling improves classification accuracy

Method handles noisy biometric sensor data

Abstract

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.