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Published on: December 11, 2019
Detection of heart rate using smartphone gyroscope data: a scoping review
Wenshan Wu1,2, Mohamed Elgendi1, Richard Ribon Fletcher3
1Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
Insights
Gyrocardiography (GCG) using smartphone sensors offers a promising, yet overlooked, method for heart rate (HR) measurement. This review highlights GCG
Area of Science:
- Biomedical Engineering
- Wearable Technology
- Cardiovascular Health
Background:
- Heart rate (HR) monitoring is crucial for diagnosing health conditions, but traditional methods like electrocardiography (ECG) have practical limitations.
- Emerging smartphone technologies like photoplethysmography (PPG) and seismocardiography (SCG) offer convenience but are susceptible to motion artifacts.
- Gyrocardiography (GCG) using smartphone gyroscope data presents a largely unexplored alternative for HR measurement.
Purpose of the Study:
- To conduct a scoping review of literature on heart rate estimation using smartphone gyroscope data.
- To analyze methods for data collection, signal pre-processing, and HR estimation in GCG studies.
- To identify challenges and future directions for GCG in HR monitoring.
Main Methods:
- Literature search for HR measurement using smartphone gyroscope data.
- Inclusion of seven relevant articles published between December 2012 and January 2023.
- Analysis of data collection, signal pre-processing, and HR estimation algorithms from selected studies.
Main Results:
- Seven articles were included, with sample sizes ranging from 11 to 435 participants.
- Inconsistent algorithms and a lack of standardized performance evaluation were observed across studies.
- Five studies lacked performance evaluation, and two used different reference signals and metrics for accuracy assessment.
Conclusions:
- Smartphone GCG shows potential for convenient HR monitoring, overcoming limitations of other methods.
- There is a need for standardized algorithms and performance evaluation metrics for GCG-based HR estimation.
- Further research is required to address challenges and establish GCG as a reliable HR monitoring tool.
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.
