ArXiv Preprint
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 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 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.
Chiraphat Boonnag, Wanumaidah Saengmolee, Narongrid Seesawad, Amrest Chinkamol, Saendee Rattanasomrerk, Kanyakorn Veerakanjana, Kamonwan Thanontip, Warissara Limpornchitwilai, Piyalitt Ittichaiwong, Theerawit Wilaiprasitporn
2022-12-09
