Recognition of the Impulse of Love at First Sight Based on Electrocardiograph Signal

Abstract

The impulse of love at first sight (ILFS) is a well known but rarely studied phenomenon. Despite the privacy of these emotions, knowing how attractive one finds a partner may be beneficial for building a future relationship in an open society, where partners are accepting each other. Therefore, this study adopted the electrocardiograph (ECG) signal collection method, which has been widely used in wearable devices, to collect signals and conduct corresponding recognition analysis. First, we used photos to induce ILFS and obtained ECG signals from 46 healthy students (24 women and 22 men) in a laboratory. Second, we extracted the time- and frequency-domain features of the ECG signals and performed a nonlinear analysis. We subsequently used a feature selection algorithm and a set of classifiers to classify the features. Combined with the sequence floating forward selection and random forest algorithms, the identification accuracy of the ILFS was 69.07%. The sensitivity, specificity, F1, and area under the curve of the other parameters were all greater than 0.6. The classification of ECG signals according to their characteristics demonstrated that the signals could be recognized. Through the information provided by the ECG signals, it can be determined whether the participant possesses the desire to fall in love, helping to determine the right partner in the fastest time; this is conducive to establishing a romantic relationship.

Is Mate Preference Recognizable Based on Electroencephalogram Signals? Machine Learning Applied to Initial Romantic Attraction

“…In general, a total of 915 (640 PSFs and 275 ASs) EEG features were extracted. save computational resources, we conducted necessary feature selection before classification (Lu et al, 2020;Zhang et al, 2021). The paired sample t-test was used to screen out the feature subsets with significant differences between the IRA engendered and IRA un-engendered categories.…”

“…To use the entire dataset to train and test the classifier, we used a nested 10-fold cross-validations to obtain reliable model estimates for feature selection and model training (Pourmohammadi and Maleki, 2020;Zhang et al, 2021). Specifically, the inner loop was responsible for selecting the optimal subset of features (Figure 4).…”

“…Thus, these signals open up new possibilities for identifying users' emotional responses and preferences for potential partners. For instance, Zhang et al (2021) successfully identified participants' initial romantic interest to potential partners based on the features extracted from electrocardiogram signals, while Lu et al (2020) successfully detected participants' initial romantic desire to potential romantic partners based on the information extracted from photoplethysmogram signals. These results demonstrate that IRA, as an important part of human emotion, can be recognized on the basis of periphery physiological signals (Lu et al, 2020;Zhang et al, 2021).…”

Research on Driving Fatigue Alleviation Using Interesting Auditory Stimulation Based on VMD-MMSE

“…Considering the accuracy and reliability of the objective detection method of driving fatigue, more and more researchers have applied this method to their own research [11,12]. In recent years, there have been more and more studies on fatigued driving using drivers' physiological signals, such as EEG [13,14], EOG [15] EMG [16] and ECG methods [17,18]. Fatigue detection method based on EEG characteristics is recognized as the gold standard by researchers [19].…”