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

Yetong Cao, Huijie Chen, Fan Li, Yu Wang

Research output: Contribution to conferencePaperpeer-review

16 Citations (Scopus)

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
Pages378-391
Number of pages14
DOIs
Publication statusPublished - 2021
Event27th ACM Annual International Conference On Mobile Computing And Networking, MobiCom 2021 - New Orleans, United States
Duration: 25 Oct 202129 Oct 2021

Conference

Conference27th ACM Annual International Conference On Mobile Computing And Networking, MobiCom 2021
Country/TerritoryUnited States
CityNew Orleans
Period25/10/2129/10/21

Keywords

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

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