Lie Detection using detection, ECG and GSR sensor readings Dataset

 This study examined the effectiveness of a detection-based lie detection method that determines lying conditions based on facial autonomic reactions. This technique combines with two other lie detection techniques using a multi sensor fusion technique that is used in the polygraph test to differentiate moments of participants lying and telling the truth about a picked-up card from a deck of cards. Experiments were conducted with 19 participants sitting in front of a camera connected to Galvanic Skin Response (GSR) probes and ECG probes for a polygraph test.  This data sets are of 19 participants who participated in a polygraph test.The collected data are named as P1 dataset.csv, ..... , P19 dataset.csv.each of the dataset has MLII, GSR signals that determines neural autonomic reactions for lie detection, and gaze (Pitch + Yaw), blinking ratio, and lip ratio that determines facial autonomic reactions for lie detection.本研究考察了一种基于检测的测谎方法的有效性，该方法通过面部自主反应判定说谎情境。该技术结合了另外两种测谎技术，并采用了测谎仪测试中常用的多传感器融合技术，以区分参与者在对从一副卡牌中抽取的指定卡牌进行说谎或如实陈述时的行为时刻。本实验共招募19名参与者，受试者坐在连接有皮肤电反应（Galvanic Skin Response, GSR）探头与心电图（Electrocardiogram, ECG）探头的摄像头前完成测谎测试。 本数据集包含19名参与测谎测试的参与者的数据。所采集的数据以P1 dataset.csv、……、P19 dataset.csv的形式命名。每份数据集均包含MLII信号、GSR信号（用于表征测谎所需的神经自主反应），以及表征测谎所需的面部自主反应的视线（俯仰角+偏航角）、眨眼率与唇部比率。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于谎言检测的多模态传感器数据集，包含19名参与者的ECG（心电图）和GSR（皮电反应）信号，以及面部自主反应数据（如注视、眨眼和嘴唇比率）。数据以CSV格式提供，适用于信号处理、机器学习和可穿戴传感等领域的研究，旨在通过多传感器融合技术区分说谎和说真话的时刻。