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
- Annu Int Conf Ieee Eng Med Biol Soc
Volume / Issue
- 2015 /
Start / End Page
- 6708 - 6711
PubMed ID
- 26737832
Pubmed Central ID
- 26737832
Electronic International Standard Serial Number (EISSN)
- 2694-0604
Digital Object Identifier (DOI)
- 10.1109/EMBC.2015.7319932
Language
- eng
Conference Location
- United States