Machine learning methods for credibility assessment of interviewees based on posturographic data.

Published

Journal Article

This paper discusses the advantages of using posturographic signals from force plates for non-invasive credibility assessment. The contributions of our work are two fold: first, the proposed method is highly efficient and non invasive. Second, feasibility for creating an autonomous credibility assessment system using machine-learning algorithms is studied. This study employs an interview paradigm that includes subjects responding with truthful and deceptive intent while their center of pressure (COP) signal is being recorded. Classification models utilizing sets of COP features for deceptive responses are derived and best accuracy of 93.5% for test interval is reported.

Full Text

Duke Authors

Cited Authors

  • Saripalle, SK; Vemulapalli, S; King, GW; Burgoon, JK; Derakhshani, R

Published Date

  • 2015

Published In

Volume / Issue

  • 2015 /

Start / End Page

  • 6708 - 6711

PubMed ID

  • 26737832

Pubmed Central ID

  • 26737832

International Standard Serial Number (ISSN)

  • 1557-170X

Digital Object Identifier (DOI)

  • 10.1109/EMBC.2015.7319932

Language

  • eng

Conference Location

  • United States