Skip to main navigation Skip to search Skip to main content

Crisp-BP: Continuous wrist PPG-based blood pressure measurement

  • Yetong Cao
  • , Huijie Chen
  • , Fan Li
  • , Yu Wang
  • Beijing Institute of Technology
  • Temple University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Arterial blood pressure (ABP) monitoring using wearables has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension. However, existing schemes mainly monitor ABP at discrete time intervals, involve some form of user effort, have insufficient accuracy, and require collecting sufficient training data for model development. To tackle these problems, we propose Crisp-BP, a novel ABP monitoring system leveraging the PPG sensor available in commercial wrist-worn devices (e.g., smartwatches or fitness trackers). It enables continuous, accurate, user-independent ABP monitoring and requires no behavior changes during collecting PPG data. The basic idea is to illuminate a skin/tissue, measure the light absorption, and characterize ABP-related blood volume change in the artery. To obtain accurate measurements and relieve the pain of training data collection, we use an arterial pulse extraction method that removes interference caused by capillary pulses. Moreover, we design a contact pressure estimation method to combat the deficiency of PPG waveform being sensitive to the contact pressure between the sensor and the skin. In addition, we leverage the great power of Bidirectional Long Short Term Memory and design a hybrid neural network model to enable user-independent ABP monitoring, so that users do not have to provide training data for model development. Furthermore, we propose a transfer learning method that first extracts general knowledge from online PPG data, then use it to improve the learning of a new model on our target problem. Extensive experiments with 35 participants demonstrate that Crisp-BP obtains the average estimation error of 0.86 mmHg and 1.67 mmHg and the standard deviation error of 6.55 mmHg and 7.31 mmHg for diastolic pressure and systolic pressure, respectively. These errors are within the acceptable range regulated by the FDA's AAMI protocol, which allows average errors of up to 5 mmHg and a standard deviation of up to 8 mmHg. Our results demonstrate that Crisp-BP is promising for improving the diagnosis and control of hypertension as it provides continuousness, comfort, convenience, and accuracy.

Original languageEnglish
Title of host publicationACM MobiCom 2021 - Proceedings of the 27th ACM Annual International Conference On Mobile Computing And Networking
PublisherAssociation for Computing Machinery
Pages378-391
Number of pages14
ISBN (Electronic)9781450383424
DOIs
Publication statusPublished - 25 Oct 2021
Event27th ACM Annual International Conference On Mobile Computing And Networking, MobiCom 2021 - New Orleans, United States
Duration: 28 Mar 20221 Apr 2022

Publication series

NameProceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
ISSN (Print)1543-5679

Conference

Conference27th ACM Annual International Conference On Mobile Computing And Networking, MobiCom 2021
Country/TerritoryUnited States
CityNew Orleans
Period28/03/221/04/22

Keywords

  • BLSTM
  • PPG sensing
  • blood pressure
  • contact pressure
  • neural networks

Fingerprint

Dive into the research topics of 'Crisp-BP: Continuous wrist PPG-based blood pressure measurement'. Together they form a unique fingerprint.

Cite this