PACMAN: A Framework for Pulse Oximeter Digit Detection and Reading in a Low-Resource Setting

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 pandemic accelerated human-machine collaboration (PACMAN) 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 data sets, notably the correctly orientated image data set. 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 data sets. The proposed framework has been currently integrated into the patient monitoring system utilized by hospitals nationwide.

I. Introduction

The COVID-19 had a substantial impact on the healthcare industry, resulting in the growth of remote health monitoring as an effective strategy for delivering high-quality care. Telemedicine and remote health monitoring enabled healthcare providers to diagnose, treat, and monitor patients without in-person visits, thereby eliminating direct physical contact and decreasing the chance of COVID-19 transmission [1]. A COVID-19 remote patient monitoring program recommended the usage of home pulse oximetry monitoring in several countries to prevent hospital overcrowding [2], [3], [4].