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

Sashi K. Saripalle; Spandana Vemulapalli; Gregory W. King; Judee K Burgoon; Reza Derakhshani

doi:10.1109/EMBC.2015.7319932

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

Sashi K. Saripalle, Spandana Vemulapalli, Gregory W. King, Judee K Burgoon, Reza Derakhshani

Management of Information, Center for

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6708-6711
Number of pages	4
Volume	2015-November
ISBN (Print)	9781424492718
DOIs	https://doi.org/10.1109/EMBC.2015.7319932
State	Published - Nov 4 2015
Event	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy Duration: Aug 25 2015 → Aug 29 2015

Other

Other	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country/Territory	Italy
City	Milan
Period	8/25/15 → 8/29/15

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Signal Processing
Biomedical Engineering
Health Informatics

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10.1109/EMBC.2015.7319932

Cite this

Saripalle, S. K., Vemulapalli, S., King, G. W., Burgoon, J. K., & Derakhshani, R. (2015). Machine learning methods for credibility assessment of interviewees based on posturographic data. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (Vol. 2015-November, pp. 6708-6711). [7319932] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2015.7319932

Machine learning methods for credibility assessment of interviewees based on posturographic data. / Saripalle, Sashi K.; Vemulapalli, Spandana; King, Gregory W.; Burgoon, Judee K; Derakhshani, Reza.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol. 2015-November Institute of Electrical and Electronics Engineers Inc., 2015. p. 6708-6711 7319932.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Saripalle, SK, Vemulapalli, S, King, GW, Burgoon, JK & Derakhshani, R 2015, Machine learning methods for credibility assessment of interviewees based on posturographic data. in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. vol. 2015-November, 7319932, Institute of Electrical and Electronics Engineers Inc., pp. 6708-6711, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, Milan, Italy, 8/25/15. https://doi.org/10.1109/EMBC.2015.7319932

Saripalle SK, Vemulapalli S, King GW, Burgoon JK, Derakhshani R. Machine learning methods for credibility assessment of interviewees based on posturographic data. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol. 2015-November. Institute of Electrical and Electronics Engineers Inc. 2015. p. 6708-6711. 7319932 https://doi.org/10.1109/EMBC.2015.7319932

Saripalle, Sashi K. ; Vemulapalli, Spandana ; King, Gregory W. ; Burgoon, Judee K ; Derakhshani, Reza. / Machine learning methods for credibility assessment of interviewees based on posturographic data. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol. 2015-November Institute of Electrical and Electronics Engineers Inc., 2015. pp. 6708-6711

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abstract = "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.",

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

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