# Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network
## 1 Introduction  
Emotions are conscious and/or unconscious feelings about a phenomenon or work, and are linked to mood, disposition, personality, and motivation. They can be expressed through various biological and physical reactions, including facial expressions, voice, text, gestures, and biosignals. Although human-computer interaction (HCI) has advanced, it remains challenging to identify human emotional states, which makes it difficult for machines to give the impression that they truly understand people [1; 2; 3; 4]. Therefore, mutual sympathy between humans and machines is an important issue, and emotion recognition is a crucial element for HCI [5; 6]. It is no exaggeration to say that considering human feelings can provide significant merits by inspiring confidence in modern complicated society. Moreover, detecting short moments of emotions is becoming important because emotion changes easily and quickly. Emotional analysis has been investigated in various ways as technology has evolved, e.g., facial images, tone of voice, heart rate, etc. [7; 8; 9]. Among sensory data, physiological signals, such as electroencephalography (EEG), electromyograms (EMGs), photoplethysmograms (PPGs), respiration pattern (RSP), and electrocardiograms (ECGs) are key factors for emotion recognition, because these are spontaneous reactions, i.e., people cannot generally control these responses intentionally [10; 11]. For example, if someone does not reveal their feelings through their facial expressions, i.e., \"Poker face\", or does not say anything, it is difficult to detect their real emotion from their external appearance. However, their physiological signals, such as respiration or heart rate, are more specific,because they are involuntary physical manifestations and contain considerable information about their emotional reaction. Emotion recognition using physiological signals has been an active research field, developing many methods [12; 13; 14], but tending to two main approaches: classifying emotions based on hand-crafted features or deep learning frameworks to extract features. [PERSON] et al. extracted many hand-crafted features from four-channel biosensors to classify emotions [15]. [PERSON] et al. proposed Legendre and Krawtchouk moments to extract features [16], and [PERSON] et al. compared EEG features for emotion classification [17]. [PERSON] et al. reviewed feature extraction methods from EEG signals and selected features that were useful for emotion recognition [18]. [PERSON] et al. proposed an emotion recognition method using multivariate extension empirical mode decomposition (MEMD) based on EEG signals [19]. However, methods using hand-crafted features had disadvantages, such as the recognition period being long and the accuracy being low. As researchers began to use deep learning methods, many studies have employed deep learning frameworks as classifiers, e.g., [PERSON] et al. proposed a deep learning model to identify feature correlation [20]. After deep learning method evolving, it has achieved remarkable outcomes in feature extraction and has been used to extract features from biosignals. [PERSON] et al. constructed physiological models to extract features using a convolutional neural network (CNN) and an auto-encoder [21]. [PERSON] et al. proposed a long-short term memory (LSTM) model to learn EEG features and classify emotion depending on arousal and valence values [22]. [PERSON] et al. introduced a parallel model of a recurrent neural network (RNN) and a CNN based on EEG signals to obtain meaningful features [23]. With deep learning, emotion recognition within one minute became possible. This study employed plethysmograms (PPGs) to classify short-term emotions. PPGs measure changes in blood volume using an optical sensor, with the fingertip being the most reliable measurement position. We selected PPG signals because they contain rich information regarding human emotion [24], and since we want to recognize short-term emotion, we need efficient information extraction from less signal.