Validation of "Depression, Anxiety, and Stress Scales” and "Changes in Psychological Distress during COVID-19” among University Students in Malaysia

Objectives: This study assessed the reliability and validity of the DASS-21 self-reported measure in the context of COVID-19 on anxiety, stress, and depression. Through this Study, the psychological effect of COVID-19 on anxiety, tension, and depression amongst samples of students enrolled in 201 Malaysian private universities was assessed. Methods: The data were collected from university students through an online survey because of Malaysian Government Movement Control Order (MCO) restrictions. Two separate intervals were used for data collection (i.e., May and September 2020), as this period was associated with the pandemic. For scale validation, convergent, discriminant, and nomological validity criteria were used. Results: The outcome of a CFA model for DASS-21 yielded factor loading that is very significant. Therefore, the measure of the root means square error approximation (RMSEA) and the comparative fit index (CFI) are acceptable values that were produced, demonstrating a good fit for the data. Conclusions: This study was conducted in the Malaysian context to validate depression, anxiety, and stress among university students using the DASS-21 scale. Our findings support the reliability of using DASS-21 in the Malaysian cultural context. Lastly, we testified to the presence of depression, anxiety, and stress among university students through descriptive statistics and provided empirical evidence in this regard. Our results suggested that there was a significant presence of DASS among university students. © 2023 by the authors.

Measuring Accuracy Towards Facial Video Heart-Rate Estimation Using Haar-Cascade and CNN Method

COVID-19 leads us to have a social distancing even for health-treatment. In this study, we attempt to estimate heart rates in humans using camera-based remote photoplethysmography (rPPG) methods, which are named after conventional PPG methods. The basic concept is focused on capturing minute variations in skin color during the human body's cardiac cycle, which involves the inflow and outflow of blood from the heart to other body parts. We have compared the performance of different methods of Blind Source Separation and face detection which form an integral part in accurately calculating the heart rate. Purpose: The purpose of this method was comparing the actual heart rate with a tuned parameter of Face Video Heart Rate estimation with CNN and OpenCV haar-cascade. Patients and methods: Videos in the dataset are run through a face detection model to get the region of interest for heart rate calculation. Source signals are converted to frequency domain for filtering and peak detection to obtain heart rate estimates Results: Face segmentation using Convolution Neural Network gives better results than the Haar Cascade OpenCV face detection module, which is as expected. Conclusion: Face segmentation using Convolution Neural Network gives better results than the Haar Cascade OpenCV face detection module. CNNs are slower to detect faces than the Open-CV module. Choosing an ROI by segmenting out facial pixels helped to keep the outliers low and therefore increased the robustness. © 2022 IEEE.