The Application of Image Recognition and Machine Learning to Capture Readings of Traditional Blood Pressure Devices: A Platform to Promote Population Health Management to Prevent Cardiovascular Diseases

Lee, Helen W.Y.Chu, Christopher T.K.Yiu, Karen K.L.Tsoi, Kelvin2020-12-242020-12-242021-01-05978-0-9981331-4-0http://hdl.handle.net/10125/71034Digital solutions for Blood Pressure Monitoring (or Telemonitoring) have sprouted in recent years, innovative solutions are often connected to the Internet of Things (IoT), with mobile health (mHealth) platform. However, clinical validity, technology cost and cross-platform data integration remain as the major barriers for the application of these solutions. In this paper, we present an IoT-based and AI-embedded Blood Pressure Telemonitoring (BPT) system, which facilitates home blood pressure monitoring for individuals. The highlights of this system are the machine learning techniques to enable automatic digits recognition, with F1 score of 98.5%; and the cloud-based portal developed for automated data synchronization and risk stratification. Positive feedbacks on trial implementation are received from three clinics. The overall system architecture, development of machine learning model in digit identification and cloud-based telemonitoring are addressed in this paper, alongside the followed implications.10 pagesEnglishAttribution-NonCommercial-NoDerivatives 4.0 InternationalBig Data on Healthcare Applicationdigital detectionimage recognitionresidual neutral networktransfer learningyoloThe Application of Image Recognition and Machine Learning to Capture Readings of Traditional Blood Pressure Devices: A Platform to Promote Population Health Management to Prevent Cardiovascular Diseases10.24251/HICSS.2021.418