# Asian Affective and Emotional State (A2 ES) Dataset of ECG and PPG for Affective Computing Research
## 1 Introduction  
As the World Health Organization's Director-General, [PERSON] remarked, in 2020, that mental health is essential for overall health and well-being [1]. The outbreak of the COVID-19 pandemic brought new challenges to the issue of mental health. According to the Kaiser Family Foundation's investigation into the effect of COVID-19 on American life, the respondents were concerned about losing income due to the fact of job loss, workplace closure, or reduced job hours during the pandemic [2]. Six out of ten adults were concerned about getting an infection or exposing themselves or their family to the virus while working. All of these concerns have negative effects on mental health and emotions. Additionally, according to a survey conducted by [PERSON]'s team [3], 71% of students in the United States claimed that their anxiety and stress levels increased as a result of the pandemic. A report from the University of Saskatchewan, Canada [4], focusing on the university's medical students, also showed a similar result. These findings demonstrate the seriousness of the COVID-19 pandemic's impacts on mental health. Therefore, in this challenging era of COVID-19, research on intelligent systems that monitor for symptoms of unpleasant emotions building up in a person is becoming more pressing. An emotion recognition system (ERS) can recognise human emotions and can be used in many different fields. For example, a stress detector that assesses employees' stresslevels using electrocardiogram (ECG) and galvanic skin response (GSR) is proposed in [5]. A cardiac-based ERS is proposed in [6; 7; 8] to assess driver stress levels and drowsiness detection in [9]. Additionally, ERS have been proposed for various uses in the education industry. In [10], voice-based emotion identification for affective e-learning is proposed. A facial ERS that enables teachers to monitor students' moods throughout class [11] and physiological signal-based ERS adoption in an intelligent tutoring system (ITS) [12] are also found among the works that reported the usage of ERS for education. From the works discussed above, it can be observed that an ERS can be built using multiple modalities: ECG, GSR, and voice and facial images. Notably, physiological signals are commonly used. Among the physiological signals that are often utilised as ERS modalities are electroencephalogram (EEG) [13; 14] and ECG [15; 16; 17; 18]. Some works integrate several modalities for their ERS [19; 20; 21], while others use a single modality [17; 22; 23]. Due to the high demand, the number of works on physiological-based ERS utilising wearable devices and noninvasive sensors has also increased. Physiological-based ERS are good for social masking avoidance [24] and are less prone to fake emotions and manipulation [25]. The utilisation of wearable devices is supported by the popularity of their usage among consumers. Rock Health surveyed digital health adoption and discovered that wearable device usage has increased significantly, from 24% in 2018 to 33% in 2019 [26]. Additionally, Statista, a German-based online statistics source predicted that the number of smartwatch users is expected to reach 1.2 million by 2024 [27]. According to this statistic, the endeavour to build ERS using wearable devices represents a path towards a proper future with significant advancements. Many labelled emotion databases have been produced in recent years that comprise various modalities [28], for example, a database for emotion analysis using physiological signals (DEAP) [13]; a database for affect, personality, and mood research on individuals and groups (AMIGOS) [18]; a database for decoding affective physiological responses (DECAF) [20]; and a multimodal physiological emotion database for discrete emotion recognition (MPED) [16].