Convolutional Autoencoder for Real-Time PPG Based Blood Pressure Monitoring Using TinyML

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose an efficient and robust convolutional autoencoder (CAE) model for continuous realtime blood pressure (BP) monitoring. The proposed model was implemented on a resource-constrained edge device. The model was built to capture the hidden patterns among successive segments and alleviate the effects of momentary glitches and outliers. The model was deployed and assessed on the Arduino Nano 33 BLE Sense in a real-time environment by means of Tiny Machine Learning (TinyML). Extensive results revealed that the proposed model improved BP prediction accuracy on both offline and real-time experiments. With 4 features, the model achieved a mean absolute error±standard deviation (MAE±SD) of 2.81±2.84 and 1.51±1.85 mmHg for systolic BP (SBP) and diastolic BP (DBP), respectively, on a dataset of 40 subjects. Whereas microcontroller unit (MCU) based real-time continuous predictions attained 2.25±2.82 for SBP and 5.01±2.10 mmHg for DBP, on 8 volunteers. Compared to the state-of-the-art models implemented on tiny devices, our model showed superior robustness and accuracy. Overall, the study offered some important insights into the significance of compact and impactful feature set and the effectiveness of the proposed model in a real-time setting.

Original languageEnglish
Title of host publication2023 International Conference on Microelectronics, ICM 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages41-45
Number of pages5
ISBN (Electronic)9798350380828
DOIs
Publication statusPublished - 2023
Event2023 International Conference on Microelectronics, ICM 2023 - Abu Dhabi, United Arab Emirates
Duration: Nov 17 2023Nov 20 2023

Publication series

NameProceedings of the International Conference on Microelectronics, ICM

Conference

Conference2023 International Conference on Microelectronics, ICM 2023
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period11/17/2311/20/23

Keywords

  • Arduino Nano
  • Blood Pressure
  • Convolutional Autoencoder
  • MCU
  • PPG
  • TinyML

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Convolutional Autoencoder for Real-Time PPG Based Blood Pressure Monitoring Using TinyML'. Together they form a unique fingerprint.

Cite this