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All Journal Buletin Ilmiah Sarjana Teknik Elektro Indonesian Journal of Electrical Engineering and Computer Science
Nor Salwa Damanhuri
Universiti Teknologi MARA (UiTM)
Electrical & Electronics Engineering

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Assessing SB effort via a non-invasive model-based method in mechanically ventilated patients in Malaysian ICU hospital Nor Salwa Damanhuri; Noor Najwa Noor Ariffin; Nor Azlan Othman; Yeong Shiong Chiew; Azrina Md Ralib; Mohd Basri Mat Noor; Paul D. Docherty; J. Geoffrey Chase
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i3.pp1232-1240

Abstract

Patients with Acute Respiratory Distress Syndrome (ARDS) required mechanical ventilation (MV) for breathing support. However, some MV patients encountered spontaneous breathing (SB) efforts while fully sedated which can obscure the true underlying respiratory mechanics of these patients. Thus, a model-based method is required to reconstruct the missing pressure and calculate the breathing effort that produced by the patients without additional clinical protocols or invasive procedure. In this paper, results of spontaneous breathing effort in Malaysian critically-ill patients adopting the developed pressure reconstruction model are presented. By using the pressure reconstruction model, the SB affected pressure waveform is reconstructed to approximate true respiratory mechanics and quantifies the SB effort. The SB breathing efforts were computed and compared with the results from Christchurch Hospital, New Zealand. The substitute measure of SB effort can be indicated from the difference between the reconstructed and unreconstructed pressure. Results shows that all patients from both cohorts exhibited SB effort with the highest SB effort at 11.48% for Malaysian patient and 21.07% for Christchurch patient. Overall, the well-developed non-invasive pressure reconstruction method is able to measure the SB effort produced by Malaysian MV patients that help the clinicians in selecting the optimal MV setting. This first non-invasive guidance in selecting the optimal setting of MV in Malaysia is potentially reduced the ICU cost and improve the MV management in Malaysian hospital.
Real-Time BISINDO Alphabet Recognition via Faster R-CNN Incorporating Skin Tone Diversity as a Classification Feature Lilis Nur Hayati; Anik Nur Handayani; Wahyu Sakti Gunawan Irianto; Rosa Andrie Asmara; Dolly Indra; Nor Salwa Damanhuri
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.15587

Abstract

Indonesian Sign Language (Bahasa Isyarat Indonesia/BISINDO) enables communication for deaf individuals through hand gestures, yet limited public awareness creates significant barriers between deaf and hearing communities. Existing recognition systems often fail to generalize across diverse skin tones, reducing their effectiveness in inclusive real-world deployment. The contribution of this research is a BISINDO alphabet recognition system that integrates skin color features - extracted via HSV-based skin segmentation - as an additional preprocessing layer within the Faster R-CNN framework, explicitly improving detection robustness across varied skin tones. The dataset consists of 8,000 images from ten adult actors representing light, medium-brown, and dark skin tones, augmented through flipping and brightness variation, with a 90:10 training-to-testing ratio. The model was trained over 15,000 steps with a batch size of 24, selected through empirical validation to balance convergence stability and dataset size. Experimental results show that indoor conditions outperform outdoor settings due to controlled lighting. Light-skinned and dark-skinned participants achieved the highest accuracy of 87.5% and F1-score of 85.71%, while medium-brown-skinned participants showed slightly lower performance, likely attributed to greater variability in reflectance under mixed lighting. The system achieves 24 frames per second, demonstrating potential for real-time communication support. These findings confirm that Faster R-CNN with skin color feature integration is effective for BISINDO alphabet recognition, with skin tone diversity being a critical performance factor. Future work will explore larger participant pools and dynamic gesture recognition under varied real-world lighting scenarios.
Co-Authors Anik Nur Handayani Azrina Md Ralib Dolly Indra J. Geoffrey Chase lilis nurhayati Mohd Basri Mat Noor Noor Najwa Noor Ariffin Nor Azlan Othman Paul D. Docherty Rosa Andrie Asmara Wahyu Sakti Gunawan Irianto Yeong Shiong Chiew