551 research outputs found
Accurate wearable heart rate monitoring during physical exercises using PPG
Objective: The challenging task of heart rate (HR) estimation from the photoplethysmographic (PPG) signal, during intensive physical exercises is tackled in this paper. Methods: The study presents a detailed analysis of a novel algorithm (WFPV) that exploits a Wiener filter to attenuate the motion artifacts, a phase vocoder to refine the HR estimate and user-adaptive postprocessing to track the subject physiology. Additionally, an offline version of the HR estimation algorithm that uses Viterbi decoding is designed for scenarios that do not require online HR monitoring (WFPV+VD). The performance of the HR estimation systems is rigorously compared with existing algorithms on the publically available database of 23 PPG recordings. Results: On the whole dataset of 23 PPG recordings, the algorithms result in average absolute errors of 1.97 and 1.37 BPM in the online and offline modes, respectively. On the test dataset of 10 PPG recordings which were most corrupted with motion artifacts, WFPV has an error of 2.95 BPM on its own and 2.32 BPM in an ensemble with 2 existing algorithms. Conclusion: The error rate is significantly reduced when compared with the state-of-the art PPG-based HR estimation methods. Significance: The proposed system is shown to be accurate in the presence of strong motion artifacts and in contrast to existing alternatives has very few free parameters to tune. The algorithm has a low computational cost and can be used for fitness tracking and health monitoring in wearable devices. The Matlab implementation of the algorithm is provided online
Harmonic Sum-based Method for Heart Rate Estimation using PPG Signals Affected with Motion Artifacts
Wearable photoplethysmography has recently become a common technology in heart rate (HR) monitoring. General observation is that the motion artifacts change the statistics of the acquired PPG signal. Consequently, estimation of HR from such a corrupted PPG signal is challenging. However, if an accelerometer is also used to acquire the acceleration signal simultaneously, it can provide helpful information that can be used to reduce the motion artifacts in the PPG signal. By dint of repetitive movements of the subjects hands while running, the accelerometer signal is found to be quasi-periodic. Over short-time intervals, it can be modeled by a finite harmonic sum (HSUM). Using the HSUM model, we obtain an estimate of the instantaneous fundamental frequency of the accelerometer signal. Since the PPG signal is a composite of the heart rate information (that is also quasi-periodic) and the motion artifact, we fit a joint HSUM model to the PPG signal. One of the harmonic sums corresponds to the heart-beat component in PPG and the other models the motion artifact. However, the fundamental frequency of the motion artifact has already been determined from the accelerometer signal. Subsequently, the HR is estimated from the joint HSUM model. The mean absolute error in HR estimates was 0.7359 beats per minute (BPM) with a standard deviation of 0.8328 BPM for 2015 IEEE Signal Processing cup data. The ground-truth HR was obtained from the simultaneously acquired ECG for validating the accuracy of the proposed method. The proposed method is compared with four methods that were recently developed and evaluated on the same dataset
The 2023 wearable photoplethysmography roadmap
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology
An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data
The current gold standard for human activity recognition (HAR) is based on
the use of cameras. However, the poor scalability of camera systems renders
them impractical in pursuit of the goal of wider adoption of HAR in mobile
computing contexts. Consequently, researchers instead rely on wearable sensors
and in particular inertial sensors. A particularly prevalent wearable is the
smart watch which due to its integrated inertial and optical sensing
capabilities holds great potential for realising better HAR in a non-obtrusive
way. This paper seeks to simplify the wearable approach to HAR through
determining if the wrist-mounted optical sensor alone typically found in a
smartwatch or similar device can be used as a useful source of data for
activity recognition. The approach has the potential to eliminate the need for
the inertial sensing element which would in turn reduce the cost of and
complexity of smartwatches and fitness trackers. This could potentially
commoditise the hardware requirements for HAR while retaining the functionality
of both heart rate monitoring and activity capture all from a single optical
sensor. Our approach relies on the adoption of machine vision for activity
recognition based on suitably scaled plots of the optical signals. We take this
approach so as to produce classifications that are easily explainable and
interpretable by non-technical users. More specifically, images of
photoplethysmography signal time series are used to retrain the penultimate
layer of a convolutional neural network which has initially been trained on the
ImageNet database. We then use the 2048 dimensional features from the
penultimate layer as input to a support vector machine. Results from the
experiment yielded an average classification accuracy of 92.3%. This result
outperforms that of an optical and inertial sensor combined (78%) and
illustrates the capability of HAR systems using...Comment: 26th AIAI Irish Conference on Artificial Intelligence and Cognitive
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Monitoring of the central blood pressure waveform via a conformal ultrasonic device.
Continuous monitoring of the central-blood-pressure waveform from deeply embedded vessels, such as the carotid artery and jugular vein, has clinical value for the prediction of all-cause cardiovascular mortality. However, existing non-invasive approaches, including photoplethysmography and tonometry, only enable access to the superficial peripheral vasculature. Although current ultrasonic technologies allow non-invasive deep-tissue observation, unstable coupling with the tissue surface resulting from the bulkiness and rigidity of conventional ultrasound probes introduces usability constraints. Here, we describe the design and operation of an ultrasonic device that is conformal to the skin and capable of capturing blood-pressure waveforms at deeply embedded arterial and venous sites. The wearable device is ultrathin (240 Ī¼m) and stretchable (with strains up to 60%), and enables the non-invasive, continuous and accurate monitoring of cardiovascular events from multiple body locations, which should facilitate its use in a variety of clinical environments
Assessing the Validity of Several Heart Rate Monitors in Wearable Technology While Mountain Biking
International Journal of Exercise Science 16(7): 1440-1450, 2023. Purpose: This study sought to assess the validity of several heart rate (HR) monitors in wearable technology during mountain biking (MTB), compared to the Polar H7Ā® HR monitor, used as the criterion device. Methods: A total of 20 participants completed two MTB trials while wearing six HR monitors (5 test devices, 1 criterion). HR was recorded on a second-by-second basis for all devices analyzed. After data processing, validity measures were calculated, including 1. error analysis: mean absolute percentage errors (MAPE), mean absolute error (MAE), and mean error (ME), and 2. Correlation analysis: Linās concordance correlation coefficient (CCC) and Pearsonās correlation coefficient (r). Thresholds for validity were set at MAPE \u3c 10% and CCC \u3e 0.7. Results: The only device that was found to be valid during mountain biking was the Suunto Spartan Sport watch with accompanying HR monitor, with a MAPE of 0.66% and a CCC of 0.99 for the overall, combined data. Conclusion: If a person would like to track their HR during mountain biking, for pacing, training, or other reasons, the devices best able to produce valid results are chest-based, wireless electrocardiogram (ECG) monitors, secured by elastic straps to minimize the movement of the device, such as the Suunto chest-based HR monitor
A multi-channel opto-electronic sensor to accurately monitor heart rate against motion artefact during exercise
This study presents the use of a multi-channel opto-electronic sensor (OEPS) to effectively monitor critical physiological parameters whilst preventing motion artefact as increasingly demanded by personal healthcare. The aim of this work was to study how to capture the heart rate (HR) efficiently through a well-constructed OEPS and a 3-axis accelerometer with wireless communication. A protocol was designed to incorporate sitting, standing, walking, running and cycling. The datasets collected from these activities were processed to elaborate sport physiological effects. t-test, Bland-Altman Agreement (BAA), and correlation to evaluate the performance of the OEPS were used against Polar and Mio-Alpha HR monitors. No differences in the HR were found between OEPS, and either Polar or Mio-Alpha (both p > 0.05); a strong correlation was found between Polar and OEPS (r: 0.96, p < 0.001); the bias of BAA 0.85 bpm, the standard deviation (SD) 9.20 bpm, and the limits of agreement (LOA) from ā17.18 bpm to +18.88 bpm. For the Mio-Alpha and OEPS, a strong correlation was found (r: 0.96, p < 0.001); the bias of BAA 1.63 bpm, SD 8.62 bpm, LOA from ā15.27 bpm to +18.58 bpm. These results demonstrate the OEPS to be capable of carrying out real time and remote monitoring of heart rate
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