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- W4385301211 abstract "The majority of polygraph questions used in psychometric objectivity assessments have inadequate applicability, low validity, and lengthy assessment times. In order to improve the Trusted Multi-View Classification (TMC) and produce more objective scale confidence results, this paper proposes a framework for assessing confidence in psychological scales based on multi-modal physiological signal fusion (INSIDER). This framework uses physiological signals (i.e. ECG, Eye Movement, and ECG) to identify lying response behaviors. By using the CNN (Temporal&Spatial) deep learning framework, in the three modalities, the EEG has the best accuracy (92.67%) in detecting lying answer behaviors; the TMC-based hybrid fusion method had both high accuracy (96.32%) and high noise immunity; and high accuracy (93.59%) can be attained by using LR classifier in the confidence assessment of the combined scale. Experimental resluts demonstrated that it can replace polygraph questions. When choosing the product, INSIDER takes into account the necessity for portability, which might serve as a model for the development of future mental health scale assessment systems." @default.
- W4385301211 created "2023-07-28" @default.
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- W4385301211 date "2022-12-01" @default.
- W4385301211 modified "2023-10-16" @default.
- W4385301211 title "INSIDER: A framework for assessing confidence in psychological scales based on multi-modal physiological signal fusion" @default.
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- W4385301211 doi "https://doi.org/10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00199" @default.
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