Machine learning methods for credibility assessment of interviewees based on posturographic data.
Publication
, Journal Article
Saripalle, SK; Vemulapalli, S; King, GW; Burgoon, JK; Derakhshani, R
Published in: Annu Int Conf IEEE Eng Med Biol Soc
2015
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.
Duke Scholars
Published In
Annu Int Conf IEEE Eng Med Biol Soc
DOI
EISSN
2694-0604
Publication Date
2015
Volume
2015
Start / End Page
6708 / 6711
Location
United States
Related Subject Headings
- Young Adult
- Surveys and Questionnaires
- Posture
- Male
- Machine Learning
- Humans
- Female
- Discriminant Analysis
- Algorithms
- Adult
Citation
APA
Chicago
ICMJE
MLA
NLM
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. Annu Int Conf IEEE Eng Med Biol Soc, 2015, 6708–6711. https://doi.org/10.1109/EMBC.2015.7319932
Saripalle, Sashi K., Spandana Vemulapalli, Gregory W. King, Judee K. Burgoon, and Reza Derakhshani. “Machine learning methods for credibility assessment of interviewees based on posturographic data.” Annu Int Conf IEEE Eng Med Biol Soc 2015 (2015): 6708–11. 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. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:6708–11.
Saripalle, Sashi K., et al. “Machine learning methods for credibility assessment of interviewees based on posturographic data.” Annu Int Conf IEEE Eng Med Biol Soc, vol. 2015, 2015, pp. 6708–11. Pubmed, doi: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. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:6708–6711.
Published In
Annu Int Conf IEEE Eng Med Biol Soc
DOI
EISSN
2694-0604
Publication Date
2015
Volume
2015
Start / End Page
6708 / 6711
Location
United States
Related Subject Headings
- Young Adult
- Surveys and Questionnaires
- Posture
- Male
- Machine Learning
- Humans
- Female
- Discriminant Analysis
- Algorithms
- Adult