![]() PPG has two main components: incoherent light source and photoreceiver. On the other hand, PPG is a low-cost and noninvasive way to measure blood volume changes in a human during heart activity. ![]() As illustrated in Figure 1, the ECG comprises three primary components: P wave, QRS wave, and T wave. An ECG is used to detect the heart’s electrical activity, which starts from the sinoatrial node to contract the heart muscles for continuing the blood pumping action in the body. Both ECG and PPG recordings have been reported to be affected by skin colour. Thus, this paper introduces the Asian Affective and Emotional State (A2ES) Database consisting of ECG and PPG recordings of 47 participants from various Asian ethnicities. According to this statistic, the endeavour to build ERS using wearable devices represents a path towards a proper future with significant advancements. Additionally, Statista, a German-based online statistics source predicted that the number of smartwatch users is expected to reach 1.2 million by 2024. Rock Health surveyed digital health adoption and discovered that wearable device usage has increased significantly, from 24% in 2018 to 33% in 2019. The utilisation of wearable devices is supported by the popularity of their usage among consumers. Physiological-based ERS are good for social masking avoidance and are less prone to fake emotions and manipulation. Due to the high demand, the number of works on physiological-based ERS utilising wearable devices and noninvasive sensors has also increased. Some works integrate several modalities for their ERS, while others use a single modality. Among the physiological signals that are often utilised as ERS modalities are electroencephalogram (EEG) and ECG. Notably, physiological signals are commonly used. 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.įrom the works discussed above, it can be observed that an ERS can be built using multiple modalities: ECG, GSR, and voice and facial images. These findings demonstrate the seriousness of the COVID-19 pandemic’s impacts on mental health. A report from the University of Saskatchewan, Canada, focusing on the university’s medical students, also showed a similar result. Additionally, according to a survey conducted by Changwon Son’s team, 71% of students in the United States claimed that their anxiety and stress levels increased as a result of the pandemic. All of these concerns have negative effects on mental health and emotions. Six out of ten adults were concerned about getting an infection or exposing themselves or their family to the virus while working. 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. The outbreak of the COVID-19 pandemic brought new challenges to the issue of mental health. The proposed database is available to other researchers.Īs the World Health Organization’s Director-General, Tedros Adhanom Ghebreyesus remarked, in 2020, that mental health is essential for overall health and well-being. The SVM was found to be the best learning algorithm for the ECG data, while RF was the best for the PPG data. The performance of the systems built are presented and compared. ![]() Emotion recognition systems are built using ECG and PPG data five machine learning algorithms: support vector machine (SVM), k-nearest neighbour (KNN), naive Bayes (NB), decision tree (DT), and random forest (RF) and deep learning techniques. An analysis of the participants’ self-assessment and a list of the 25 stimuli utilised are also presented in this work. The subjects were exposed to 25 carefully selected audio–visual stimuli to elicit specific targeted emotions. ![]() The database comprises electrocardiogram (ECG) and photoplethysmography (PPG) recordings from 47 Asian participants of various ethnicities. ![]() This paper introduces an emotion recognition database, the Asian Affective and Emotional State (A2ES) dataset, for affective computing research. This is an important issue for ensuring inclusiveness and avoiding bias in this field. However, the lack of an affective database based on physiological signals from the Asian continent has been reported. This area has attracted many researchers globally. Affective computing focuses on instilling emotion awareness in machines. ![]()
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