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

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	2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6708-6711
Number of pages	4
ISBN (Electronic)	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

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2015-November
ISSN (Print)	1557-170X

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

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

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 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 (pp. 6708-6711). Article 7319932 (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.. 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. et al.
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 6708-6711 7319932 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2015-November).

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 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015., 7319932, 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., 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 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 6708-6711. 7319932. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2015.7319932

Saripalle, Sashi K. ; Vemulapalli, Spandana ; King, Gregory W. et al. / Machine learning methods for credibility assessment of interviewees based on posturographic data. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 6708-6711 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{1b2b896879ac4a929a4c8518dc78f63a,

title = "Machine learning methods for credibility assessment of interviewees based on posturographic data",

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.",

author = "Saripalle, {Sashi K.} and Spandana Vemulapalli and King, {Gregory W.} and Burgoon, {Judee K.} and Reza Derakhshani",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 ; Conference date: 25-08-2015 Through 29-08-2015",

year = "2015",

month = nov,

day = "4",

doi = "10.1109/EMBC.2015.7319932",

language = "English (US)",

series = "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",

booktitle = "2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015",

}

TY - GEN

T1 - Machine learning methods for credibility assessment of interviewees based on posturographic data

AU - Saripalle, Sashi K.

AU - Vemulapalli, Spandana

AU - King, Gregory W.

AU - Burgoon, Judee K.

AU - Derakhshani, Reza

PY - 2015/11/4

Y1 - 2015/11/4

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84953252946&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84953252946&partnerID=8YFLogxK

U2 - 10.1109/EMBC.2015.7319932

DO - 10.1109/EMBC.2015.7319932

M3 - Conference contribution

C2 - 26737832

AN - SCOPUS:84953252946

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 6708

EP - 6711

BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015

Y2 - 25 August 2015 through 29 August 2015

ER -

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

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this