PACMAN: A Framework for Pulse Oximeter Digit Detection and Reading in A Low-Resource Setting
Issued Date
2023-01-01
Resource Type
eISSN
23274662
Scopus ID
2-s2.0-85151493215
Journal Title
IEEE Internet of Things Journal
Rights Holder(s)
SCOPUS
Bibliographic Citation
IEEE Internet of Things Journal (2023)
Suggested Citation
Boonnag C., Ittichaiwong P., Saengmolee W., Seesawad N., Chinkamol A., Rattanasomrerk S., Veerakanjana K., Thanontip K., Limpornchitwilai W., Wilaiprasitporn T. PACMAN: A Framework for Pulse Oximeter Digit Detection and Reading in A Low-Resource Setting. IEEE Internet of Things Journal (2023). doi:10.1109/JIOT.2023.3262205 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/81789
Title
PACMAN: A Framework for Pulse Oximeter Digit Detection and Reading in A Low-Resource Setting
Other Contributor(s)
Abstract
In light of the COVID-19 pandemic, patients were required to manually input their daily oxygen saturation (SpO2) and pulse rate (PR) values into a health monitoring system—unfortunately, such a process trend to be an error in typing. Several studies attempted to detect the physiological value from the captured image using optical character recognition (OCR). However, the technology has limited availability with high cost. Thus, this study aimed to propose a novel framework called PACMAN (Pandemic Accelerated Human-Machine Collaboration) with a low-resource deep learning-based computer vision. We compared state-of-the-art object detection algorithms (scaled YOLOv4, YOLOv5, and YOLOR), including the commercial OCR tools for digit recognition on the captured images from the pulse oximeter display. All images were derived from crowdsourced data collection with varying quality and alignment. YOLOv5 was the best-performing model against the given model comparison across all datasets, notably the correctly orientated image dataset. We further improved the model performance with the digits auto-orientation algorithm and applied a clustering algorithm to extract SpO2 and PR values. The accuracy performance of YOLOv5 with the implementations was approximately 81.0-89.5%, which was enhanced compared to without any additional implementation. Accordingly, this study highlighted the completion of the PACMAN framework to detect and read digits in real-world datasets. The proposed framework has been currently integrated into the patient monitoring system utilized by hospitals nationwide.