Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

A Classification Model for Sensing Human Trust in Machines Using EEG and GSR Today, intelligent machines interact and collaborate with humans in a way that demands a greater level of trust between human and machine. A first step toward building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real time. In this article, two approaches for developing classifier-based empirical trust-sensor models are presented that specifically use electroencephalography and galvanic skin response measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust-sensor model based on the general feature set (i.e., a “general trust-sensor model”). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

Loading next page...
 
/lp/association-for-computing-machinery/a-classification-model-for-sensing-human-trust-in-machines-using-eeg-8hMeihsIh9

References (60)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2018 ACM
ISSN
2160-6455
eISSN
2160-6463
DOI
10.1145/3132743
Publisher site
See Article on Publisher Site

Abstract

Today, intelligent machines interact and collaborate with humans in a way that demands a greater level of trust between human and machine. A first step toward building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real time. In this article, two approaches for developing classifier-based empirical trust-sensor models are presented that specifically use electroencephalography and galvanic skin response measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust-sensor model based on the general feature set (i.e., a “general trust-sensor model”). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS)Association for Computing Machinery

Published: Nov 16, 2018

Keywords: EEG

There are no references for this article.