School of Medicine Publications and Presentations

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Background: Smartphone-based blood pressure (BP) monitor using photoplethysmogram (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control ofhypertension (HT).

Objective: This study aimed to develop a mobile personal healthcare system for non-invasive, pervasive, and continuous estimation of BP level and variability to be user-friendly to elderly.

Methods: The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless and wearable PPG-only sensor, and a native purposely-designed smartphone application using multilayer perceptron machine learning techniques from raw signals. We performed a pilot study with three elder adults (mean age 61.3 ± 1.5 years; 66% women) to test usability and accuracy of the smartphone-based BP monitor.

Results: The employed artificial neural network (ANN) model performed with high accuracy in terms of predicting the reference BP values of our validation sample (n=150). On average, our approach predicted BP measures with accuracy >90% and correlations >0.90 (P < .0001). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg.

Conclusions: With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of healthcare, particularly in rural zones, areas lacking physicians, and solitary elderly populations.


© 2020 The authors. All rights reserved.

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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Publication Title

JMIR Mhealth and Uhealth



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Mentor/PI Department

Office of Human Genetics



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