# Asian Affective and Emotional State (A2 ES) Dataset of ECG and PPG for Affective Computing Research
## 6 Discussion and Conclusions  
In this era of COVID-19 and many other challenges, developing an emotion aware system is beneficial for society's mental health. Therefore, an affective research dataset, A2 ES, was proposed in this paper. The dataset consists of ECG and PPG recordings collected from 47 Asian participants from various ethnicities using wearables and off-the-shelf devices. This was conducted to address the lack of such datasets for affective computing research and bias avoidance in future research. The participants were exposed to 25 audio-visual stimuli to elicit specific targeted emotions. The self-assessment ratings from the participants and a list of the 25 stimuli used were included, along with the ECG and PPG performance evaluations using ML and DL approaches. The findings prove the usability of the A2 ES for emotion recognition. The performance of ML in classifying emotions using the A2 ES with ECG and PPG was better than DL. This was because the size of the data was limited due to the small sample size of the A2 ES dataset. The A2 ES data are available upon request for other researchers and noncommercial purposes. Although, the data are labelled according to the seven basic emotions of neutral, happy, surprise, fear, disgust, sad, and anger, as well as their intensity, the data can be relabelled to arousal and valence. The data are not tagged to the participants. It is suggested that future research adopting the A2 ES should consider different methods of feature extraction and feature selection and reduction to ensure only informative features are applied for more accurate classification, enhanced classification algorithms, and ensemble classifiers, as well as for addressing the imbalance in the data of the different classes. Additionally, to benefit from the strength of DL, the prospective focus should be to enhance the ERS by increasing the size of the data, such as by applying a data augmentation technique to expand the size of the data. The inclusion of the A2 ES dataset with other affective computing datasets in building an ERS is expected to lead to an unbiased ERS.  
Conceptualization, N.A.A.A., T.K., S.N.M.S.I. and M.A.H.; methodology, T.K., S.N.M.S.I. and M.A.H.; software, T.K. and S.N.M.S.I.; validation, N.A.A.A. and K.A.A.; formal analysis, N.A.A., T.K. and S.N.M.S.I.; investigation, T.K., S.N.M.S.I. and M.A.H.; resources, N.A.A.A.; writing--original draft preparation, T.K., S.N.M.S.I. and M.A.H.; writing--review and editing, N.A.A.A., K.A.A., S.Z.I., A.A.A. and J.E.R.; visualization, T.K., S.N.M.S.I. and M.A.H.; supervision, N.A.A.A., S.Z.I., A.A.A. and J.E.R.; project administration, N.A.A.A. and K.A.A.; funding acquisition, N.A.A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a TM Research and Development Grant (RDTC/190988) that was awarded to the Multimedia University.  
The Multimedia University Research Ethics Committee (REC) has approved the data collection of A2 ES (approval number: EA0282021).  
The A2 ES dataset is available upon request for other researchers and noncommercial purposes at [EMAIL_ADDRESS].  
Special thanks to all participants of the A2 ES data collection exercise.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline  & **DL** \\ \hline Arousal & 34.63 \\ \hline Valence & 56.8 \\ \hline Dimensional & 24.35 \\ \hline \end{tabular}
\end{table}
Table 9: DL model’s performance for PPG.