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All Journal Journal of Applied Engineering and Technological Science (JAETS) International Journal of Accounting & Finance in Asia Pasific
Vijay Anant Athavale
Walchand Institute of Technology
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Touch n Go e-Wallet: The New Payment Style Existed when COVID-19 Hits Muhammad Daniel Bin Ahmad Izaidin; Vijay Anant Athavale; Muhammad Danial Bin Abdul Razak; Nafisa Hani Binti Mohamed Zain; Najla Awatif Binti Aqimu’ Ajiby; Sakshi Singh; Yash Rajendra Katkar
International Journal of Accounting & Finance in Asia Pasific (IJAFAP) Vol 5, No 3 (2022): October Edition of International Journal of Accounting Finance in Asia Pasific
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (234.879 KB) | DOI: 10.32535/ijafap.v5i3.1933

Abstract

Touch ‘n Go e-wallet is a smartphone application that has recently gained users since the pandemic of COVID-19 hits Malaysia. Touch ‘n Go is an e-wallet, an electronic card that can make online payments using a smartphone. It is a secure way to pay using a smartphone because it is convenient to use and reduces physical touch, which can spread diseases and germs to other people. The pandemic and the imposition of Movement Control Orders (MCO) and Home Quarantine have encouraged e-wallet usage, as people will choose cashless payments during that period. This study examines how e-wallets help consumers throughout the COVID-19 pandemic in Malaysia. A total of 150 consumers completed an online survey via Google Forms, and the data were analyzed using SPSS. We found that perceived ease of use and trust impacted consumer satisfaction. This research provides new insights on e-wallet perceptions of Touch n Go and how this perception may promote consumer satisfaction.Keywords: COVID-19, E-wallet, MCO, Pandemic, Physical touch, Smartphone, Touch n Go
Noise Reduction of Motion and EMG Artifacts in Holter ECG Using IIR Filters for Robust Arrhythmia Detection Sumber Sumber; Endang Dian S; Triwiyanto Triwiyanto; Roichatun Nashichah; Vijay Anant Athavale
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9426

Abstract

Ambulatory Holter electrocardiography (ECG) enables continuous monitoring for detecting transient arrhythmias; however, its diagnostic reliability is significantly degraded by motion artifacts and electromyographic (EMG) interference. Under severe motion artifact conditions, prior studies report that ambulatory ECG SNR can fall below −10 dB , although SNR levels vary substantially depending on activity type and electrode placement, reducing usable data segments and impairing arrhythmia detection. While advanced denoising methods such as wavelet transforms and deep learning achieve high accuracy, their computational complexity limits real-time deployment in resource-constrained embedded systems. This reveals a critical gap in lightweight methods that jointly optimize noise suppression, morphological preservation, and downstream diagnostic performance. This study proposes a computationally efficient IIR Butterworth bandpass filtering framework for real-time IoT-based Holter ECG systems. The system combines three-lead ECG acquisition, embedded processing on an ESP32, and real-time visualization. Performance is assessed using SNR, mean squared error (MSE), Pearson correlation, and confusion matrix-based detection metrics on ten male participants under controlled motion and muscle artifact conditions. Results demonstrate statistically significant SNR improvements for motion artifacts (ΔSNR = 9.47 ± 1.96 dB, t(9) = 15.28, p < 0.001) and EMG artifacts (ΔSNR = 16.73 ± 0.91 dB, t(9) = 58.11, p < 0.0001). Post-filtering morphological fidelity was high, with mean Pearson correlation of 0.963 for motion artifacts and 0.945 for muscle artifacts. These signal quality improvements translated into 95.3% post-filtering arrhythmia detection accuracy (sensitivity: ≈96.0%, specificity: ≥97.0%, F1-score: ≥95.0%), significantly exceeding the 70% minimum performance threshold adopted in this study as a conservative screening criterion (t(9) = 29.7, p < 0.001). Despite dataset limitations (n = 10), the proposed framework provides an effective trade-off between computational efficiency and diagnostic reliability, supporting scalable and real-time ambulatory ECG monitoring for early arrhythmia screening.
Co-Authors Endang Dian S Muhammad Danial Bin Abdul Razak Muhammad Daniel Bin Ahmad Izaidin Nafisa Hani Binti Mohamed Zain Najla Awatif Binti Aqimu’ Ajiby Roichatun Nashichah Sakshi Singh Sumber Sumber Triwiyanto Triwiyanto Yash Rajendra Katkar