An improved algorithm for Heart Rate tracking during physical exercise using simultaneous wrist-type photoplethysmographic (PPG) and acceleration signals

Mashhadi, Mahdi Boloursaz; Essalat, Mahmoud; Ahmadi, Mohammad Mahdi; Marvasti, Farokh

doi:10.1109/icbme.2016.7890946

Cited by 5 publications

(2 citation statements)

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“…Ten subjects were involved in the experiments (8 males, 2 females, 25.7 ± 2.8 years old). The number of participants in the experiment has been selected with reference to previous published studies concerning photoplethysmography [39], [40], [41]. The volunteers are welcomed into the LHC mockup, where environmental conditions similar to the real case are reproduced, as shown in Figure 4, and introduced to the experiment.…”

Section: B Experimental Setup and Protocolmentioning

confidence: 99%

Robot-Aided Contactless Monitoring of Workers’ Cardiac Activity in Hazardous Environment

et al. 2022

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Section: B Experimental Setup and Protocolmentioning

confidence: 99%

Robot-Aided Contactless Monitoring of Workers’ Cardiac Activity in Hazardous Environment

et al. 2022

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“…A number of authors have used adaptive filtering to first clean the PPG signals from which HR were calculated using either modeling or heuristics. Heuristic-based trackers appeared in number of studies like [33,79,[89][90][91][92][93][94][95]. Pan et al [89] employed HR modeling along with heuristics to reduce the error.…”

Section: Sptmentioning

confidence: 99%

Heart rate tracking in photoplethysmography signals affected by motion artifacts: a review

Ismail

Akram

Siddiqi

2021

EURASIP J. Adv. Signal Process.

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Non-invasive photoplethysmography (PPG) technology was developed to track heart rate during motion. Automated analysis of PPG has made it useful in both clinical and non-clinical applications. However, PPG-based heart rate tracking is a challenging problem due to motion artifacts (MAs) which are main contributors towards signal degradation as they mask the location of heart rate peak in the spectra. A practical analysis system must have good performance in MA removal as well as in tracking. In this article, we have presented state-of-art techniques in both areas of the automated analysis, i.e., MA removal and heart rate tracking, and have concluded that adaptive filtering and multi-resolution decomposition techniques are better for MA removal and machine learning-based approaches are future perspective of heart rate tracking. Hence, future systems will be composed of machine learning-based trackers fed with either empirically decomposed signal or from output of adaptive filter.

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Robust Beat-to-Beat Interval from Wearable PPG using RLS and SSA

Bhattacharjee¹,

Choudhury²,

Pal³

2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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An improved algorithm for Heart Rate tracking during physical exercise using simultaneous wrist-type photoplethysmographic (PPG) and acceleration signals

Cited by 5 publications

References 15 publications

Robot-Aided Contactless Monitoring of Workers’ Cardiac Activity in Hazardous Environment

Robot-Aided Contactless Monitoring of Workers’ Cardiac Activity in Hazardous Environment

Heart rate tracking in photoplethysmography signals affected by motion artifacts: a review

Robust Beat-to-Beat Interval from Wearable PPG using RLS and SSA

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