Detection of heart rate using smartphone gyroscope data: a scoping review
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fcvm-10-1329290.pdf
Description
Published version
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10.02 MB
Format
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Checksum (MD5)
246d74f1ef51fd46225cb9b155fa588a
Author(s)
Wu, Wenshan
Elgendi, Mohamed
Fletcher, Richard Ribon
Bomberg, Hagen
Eichenberger, Urs
Guan, Cuntai
Menon, Carlo
Date Issued
December 18, 2023
Journal
Frontiers in Cardiovascular Medicine
Publisher
Frontiers Media SA
Citation
Wu W, Elgendi M, Fletcher RR, Bomberg H, Eichenberger U, Guan C and Menon C (2023) Detection of heart rate using smartphone gyroscope data: a scoping review. Front. Cardiovasc. Med. 10:1329290.
Version
Final published version
Abstract
Heart rate (HR) is closely related to heart rhythm patterns, and its irregularity can imply serious health problems. Therefore, HR is used in the diagnosis of many health conditions. Traditionally, HR has been measured through an electrocardiograph (ECG), which is subject to several practical limitations when applied in everyday settings. In recent years, the emergence of smartphones and microelectromechanical systems has allowed innovative solutions for conveniently measuring HR, such as smartphone ECG, smartphone photoplethysmography (PPG), and seismocardiography (SCG). However, these measurements generally rely on external sensor hardware or are highly susceptible to inaccuracies due to the presence of significant levels of motion artifact. Data from gyrocardiography (GCG), however, while largely overlooked for this application, has the potential to overcome the limitations of other forms of measurements. For this scoping review, we performed a literature search on HR measurement using smartphone gyroscope data. In this review, from among the 114 articles that we identified, we include seven relevant articles from the last decade (December 2012 to January 2023) for further analysis of their respective methods for data collection, signal pre-processing, and HR estimation. The seven selected articles’ sample sizes varied from 11 to 435 participants. Two articles used a sample size of less than 40, and three articles used a sample size of 300 or more. We provide elaborations about the algorithms used in the studies and discuss the advantages and disadvantages of these methods. Across the articles, we noticed an inconsistency in the algorithms used and a lack of established standardization for performance evaluation for HR estimation using smartphone GCG data. Among the seven articles included, five did not perform any performance evaluation, while the other two used different reference signals (HR and PPG respectively) and metrics for accuracy evaluation. We conclude the review with a discussion of challenges and future directions for the application of GCG technology.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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DOI of Published Version
https://doi.org/10.3389/fcvm.2023.1329290
