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.
authors.forename: Sashi K
authors.initials: SK
authors.lastname: Saripalle
authors.forename: Spandana
authors.initials: S
authors.lastname: Vemulapalli
authors.forename: Gregory W
authors.initials: GW
authors.lastname: King
authors.forename: Judee K
authors.initials: JK
authors.lastname: Burgoon
authors.forename: Reza
authors.initials: R
authors.lastname: Derakhshani
mesh_terms: D000328:Adult; D000465:Algorithms; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D011187:Posture / Q000502:physiology*; D011795:Surveys and Questionnaires; D055815:Young Adult