Detection of heart rate using smartphone gyroscope data: a scoping review.

Front Cardiovasc Med

Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.

Published: December 2023

AI Article Synopsis

  • Heart rate (HR) irregularities can indicate serious health issues, making HR measurement essential for diagnosis, traditionally done with ECG, which has limitations in daily use.
  • Recent advancements in technology, including smartphones, have introduced new methods like smartphone ECG, PPG, and SCG, but these often suffer from inaccuracies due to movement.
  • Our review of seven studies on heart rate measurement using smartphone gyrocardiography (GCG) reveals inconsistent algorithms and a lack of standardization in performance evaluation, with most studies failing to properly assess their methods.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10757953PMC
http://dx.doi.org/10.3389/fcvm.2023.1329290DOI Listing

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