Heart rate variability estimation in photoplethysmography signals using Bayesian learning approach

Alqaraawi, Ahmed; Alwosheel, Ahmad; Alasaad, Amr

doi:10.1049/htl.2016.0006

Cited by 46 publications

(25 citation statements)

References 12 publications

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“…In addition to activity intensity, several other studies have identified that motion artifacts during exercise were negatively correlated with the accuracy of PPG heart rate-monitoring systems [32,38,46,[54][55][56]. For example, in an experiment conducted by Gillinov et al [2], the optical devices exhibited more accuracy for exercise with fewer arm motion artifacts (cycling and elliptical exercise with no arms movement).…”

Section: Principal Findingsmentioning

confidence: 99%

Accuracy of Optical Heart Rate Sensing Technology in Wearable Fitness Trackers for Young and Older Adults: Validation and Comparison Study

Chow¹,

Yang²

2020

JMIR Mhealth Uhealth

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Background Wearable fitness trackers are devices that can record and enhance physical activity among users. Recently, photoplethysmography (PPG) devices that use optical heart rate sensors to detect heart rate in real time have become popular and help in monitoring and controlling exercise intensity. Although the benefits of using optical heart rate monitors have been highlighted through studies, the accuracy of the readouts these commercial devices generate has not been widely assessed for different age groups, especially for the East Asian population with Fitzpatrick skin type III or IV. Objective This study aimed to examine the accuracy of 2 wearable fitness trackers with PPG to monitor heart rate in real time during moderate exercise in young and older adults. Methods A total of 20 young adults and 20 older adults were recruited for this study. All participants were asked to undergo a series of sedentary and moderate physical activities using indoor aerobic exercise equipment. In this study, the Polar H7 chest-strapped heart rate monitor was used as the criterion measure in 2 fitness trackers, namely Xiaomi Mi Band 2 and Garmin Vivosmart HR+. The real-time, second-by-second heart rate data obtained from both devices were recorded using the broadcast heart rate mode. To critically analyze the results, multiple statistical parameters including the mean absolute percentage error (MAPE), Lin concordance correlation coefficient (CCC), intraclass correlation coefficient, the Pearson product moment correlation coefficient, and the Bland-Altman coefficient were determined to examine the performances of the devices. Results Both test devices exhibited acceptable overall accuracy as heart rate sensors based on several statistical tests. Notably, the MAPE values were below 10% (the designated threshold) in both devices (GarminYoung=3.77%; GarminSenior=4.73%; XiaomiYoung=7.69%; and XiaomiSenior=6.04%). The scores for reliability test of CCC for Garmin were 0.92 (Young) and 0.80 (Senior), whereas those for Xiaomi were 0.76 (Young) and 0.73 (Senior). However, the results obtained using the Bland-Altman analysis indicated that both test optical devices underestimated the average heart rate. More importantly, the study documented some unexpected outlier readings reported by these devices when used on certain participants. Conclusions The study reveals that commonly used optical heart rate sensors, such as the ones used herein, generally produce accurate heart rate readings irrespective of the age of the user. However, users should avoid relying entirely on these readings to indicate exercise intensities, as these devices have a tendency to produce erroneous, extreme readings, which might misinterpret the real-time exercise intensity. Future studies should therefore emphasize the occurrence rate of such errors, as this will likely benefit the development of improved models of heart rate sensors.

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Section: Principal Findingsmentioning

confidence: 99%

Accuracy of Optical Heart Rate Sensing Technology in Wearable Fitness Trackers for Young and Older Adults: Validation and Comparison Study

Chow¹,

Yang²

2020

JMIR Mhealth Uhealth

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“…However, the complicated operations made the algorithm not suitable for wearable systems. In the time domain, the AMPD-BL algorithm in [29], jointly integrating the AMPD [15] and Bayesian learning (BL) approaches, was used to estimate the heart rate data.…”

Section: Time-frequency Analysismentioning

confidence: 99%

An Adaptive Heart Rate Monitoring Algorithm for Wearable Healthcare Devices

Kuo

Teng

et al. 2021

Electronics

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This paper focuses on developing an adaptive heart rate monitoring algorithm for wrist-based rehabilitation systems. Due to the characteristics of the wrist, the heartbeat measurements are unstable. To improve the preprocessing efficiency and perform measurement calibration, a novel joint algorithm incorporating automatic multiscale-based peak detection and fuzzy logic control (AMPD-Fuzzy) is proposed. The monitoring approach consists of two phases: (1) Preprocessing and (2) Detection and Calibration. Phase 1 explores the parameter settings, threshold, and decision rules. Phase 2 applies fuzzy logic control and the Laplacian model to provide signal reshaping. Experimental results show that the proposed algorithm can effectively achieve heart rate monitoring for wearable healthcare devices.

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“…Multi-peak detection is a type of algorithm that can find multiple local optimal points, [12][13][14][15][16] and automatic multiscale-based peak detection (AMPD) 17,18 and Fibonacci peak detection (FPD) 19 are such a multi-peak detection algorithm. [17][18][19][20][21][22] In applications, however, optimization performances and computational complexity of the multi-peak detection algorithms often depend on the parameters such as search range, search depth, and the number of sprinkling. Inappropriate parameter settings often lead to slow convergence of algorithms, failure to find extreme values.…”

Section: Related Workmentioning

confidence: 99%

A peak detection algorithm adopting magnetic sensor signal for rail spike location in tamping machine

Gao

Shen

et al. 2019

Advances in Mechanical Engineering

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When a tamping machine is tamping track ballasts under railway, it is necessary to determine the tamping positions in advance. This study proposes a peak detection algorithm to locate rail spikes with magnetic sensor signals, and then determine the tamping positions. In this algorithm, we have performed the downsampling, sliding window, threshold classification, and secondary peak search to complete peak detection. Especially, we discuss how a sliding window length, a downsampling frequency, a secondary-search-window length, and other parameters affect the performance of the algorithm. In experiments, we use a group of real magnetic sensor signals to evaluate the algorithm. Compared with traditional methods, the proposed algorithm can reduce the false positives and misses of peak detection to 0, while the maximum location error will not more than 1 cm.

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Heart rate variability estimation in photoplethysmography signals using Bayesian learning approach

Cited by 46 publications

References 12 publications

Accuracy of Optical Heart Rate Sensing Technology in Wearable Fitness Trackers for Young and Older Adults: Validation and Comparison Study

Accuracy of Optical Heart Rate Sensing Technology in Wearable Fitness Trackers for Young and Older Adults: Validation and Comparison Study

An Adaptive Heart Rate Monitoring Algorithm for Wearable Healthcare Devices

A peak detection algorithm adopting magnetic sensor signal for rail spike location in tamping machine

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