## 1 Introduction
This approach uses digitized images depicting various emotional states, asking users to select images across three dimensions from a scale of one to nine representing PAD: pleasure (happy--1 vs. unhappy--9), arousal (excited--1 vs. calm--9), and dominance (controlled--1 vs. in control--9). While the SAM facilitates non-verbal interaction, it still requires participants to have communication skills and a cognitive understanding of emotions to navigate and respond to the images [13]. In psychology and affective computing, many researchers use [PERSON]'s theory, which identifies six basic emotions: fear, anger, happiness, sadness, disgust, and surprise [16]. However, researchers sometimes substitute joy for happiness [17]. Some researchers are able to differentiate among four basic emotions [18], while others suggest that they can identify twenty-seven [19]. A systematic review examining the use of physiological signals for emotion recognition determined that only 15% of studies investigated more than four emotions [20]. While PAD scores are recognized within research environments as emotion identifiers, the colloquial terminology that represents emotion is more easily understood by the general population. However, very little research exists that maps PAD scores onto emotions. Using machine learning techniques to identify emotional clusters based on PAD scores, our current group has found that six basic emotions are distinguishable [21]. In a previous paper [15], an Emotion Identification (EI) model was developed that maps PAD scores onto one of six basic emotion clusters using the subjective words representing emotions. The autonomic nervous system responds to stimuli through psychophysiological signals, of which a person may be unaware. Emotion is interpreted via the sympathetic and parasympathetic nervous system and can cause arousing and calming feelings, respectively [22]. Arousal may be detected by an increased heart rate, increased blood flow to skin blood vessels, and/or an increased rate of breathing. Calm may be noticed by a decreased cardiac rate and respiration [22]. Electrocardiography (ECG), photoplethysmography (PPG), galvanic skin response (GSR), and respiration (RESP) sensors can allow for the detection of these signals [22]. The monitoring of physiological signals with an inexpensive wearable method could be used to detect emotions discretely without the need for an individual to explicitly express their emotions. By combining the model in this current research with the EI model that links PAD scores to basic emotions, emotions can be determined from physiological signals [15]. Many devices have been researched to detect emotion in typically developed participants in laboratory settings with good success. These devices include either single sensors or the integration of multiple sensors. [PERSON] et al. detected only three emotions (amusement, sadness, and neutral) and relied on physiological signals from 37 typically developing volunteers using PPG and GSR. The device developed by [PERSON] et al. [8] integrated EMG, ECG, RESP, and EDA (electrodermal activity--similar to GSR) sensors into a driving mask and focused on real-life driving situations to identify four emotions (high stress, low stress, disappointment, and euphoria), but the test volunteers were above average with respect to their driving skills and abilities. This focus on average or above-average participants does not adequately address the need for a device for participants who have difficulty with communication. Within affecting computing, researchers typically only categorize each emotion of pleasure, arousal, and dominance into two or three categories (high, low, and neutral) [20], but to the best of our knowledge, no researcher has attempted to categorize them into nine categories (for each of the three PAD dimensions). Most affective computing research focuses primarily on the pleasure and arousal dimensions of emotion, neglecting the dominance component.