Photoplethysmogram at green light: Where does chaos arise from?

Sviridova, Nina; Zhao, Tiejun; Aihara, Kazuyuki; Nakamura, Kazuyuki; Nakano, Akimasa

doi:10.1016/j.chaos.2018.09.016

Cited by 43 publications

(33 citation statements)

References 38 publications

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“…Motion artefacts are known to have negative influence on the accuracy of rPPG sensors. This is not the case for gPPG sensors, which are less vulnerable to the effects of motion artifacts [19,25,29].…”

Section: The Tracker and Sensor Typesmentioning

confidence: 97%

“…Another factor that could influence the accuracy of PPG sensors is the colour of the light emitting diode (LED) light that comes with the sensor. The majority of CWHDs that utilize PPG sensors for monitoring heart rate come with green light PPG (gPPG) [12,24,25]. However, red light PPG (rPPG) sensors (i.e.…”

Section: The Tracker and Sensor Typesmentioning

confidence: 99%

“…However, red light PPG (rPPG) sensors (i.e. pulse oximeters) are commonly used in clinical environments [12,[25][26][27]. rPPG sensors have a number of advantages over gPPG sensors.…”

Section: The Tracker and Sensor Typesmentioning

confidence: 99%

“…The green light in gPPG sensors emits shorter wavelengths and does not penetrate deeper into the innermost layer of the skin. In contrast, rPPG sensors can penetrate deeper into the skin because the human body does not absorb the red light [25,26]. This property enables rPPG sensors to detect other biological signals like the arterial oxygen saturation, respiration, and blood pressure [24,26,27].…”

Section: The Tracker and Sensor Typesmentioning

confidence: 99%

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A Systematic Literature Review of the Factors that Influence the Accuracy of Consumer Wearable Health Device Data

Mahloko

Adebesin

2020

Lecture Notes in Computer Science

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The use of consumer wearable health device (CWHD) for fitness tracing has seen an upward trend worldwide. CWHDs support individuals in taking ownership of their personal well-being and keeping track of their fitness goals. However, there are genuine concerns over the accuracy of the data collected by these devices. In this study, we investigated the factors that influence the accuracy of the data collected by CWHDs for heart rate measurement, physical activity (PA), and sleep monitoring using a systematic literature review. Forty-seven papers were analyzed from five electronic databases based on specific inclusion and exclusion criteria. All 47 papers that we analyzed were published by authors from developed countries. Using thematic analysis, we classified the factors that influence the accuracy of the data collected by CWHDs into three main groups, namely (i) the tracker and sensor type, (ii) the algorithm used in the device, and (iii) the limitation in the design, energy consumption, and processing capability of the device. The research results point to a dearth of studies that focus on the accuracy of the data collected by CWHDs by researchers from developing countries. Keywords: Consumer wearable health device Á CWHD Á Wearable health device Á Wearable health device data accuracy Á Systematic literature review © IFIP International Federation for Information Processing 2020

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Section: The Tracker and Sensor Typesmentioning

confidence: 97%

Section: The Tracker and Sensor Typesmentioning

confidence: 99%

“…However, red light PPG (rPPG) sensors (i.e. pulse oximeters) are commonly used in clinical environments [12,[25][26][27]. rPPG sensors have a number of advantages over gPPG sensors.…”

Section: The Tracker and Sensor Typesmentioning

confidence: 99%

Section: The Tracker and Sensor Typesmentioning

confidence: 99%

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A Systematic Literature Review of the Factors that Influence the Accuracy of Consumer Wearable Health Device Data

Mahloko

Adebesin

2020

Lecture Notes in Computer Science

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“…[3,31] In both modes, short light flashes are emitted to sample the blood volume of the moment. While there is a broad consensus on the origin of the transmission mode's signal, in which an increasing blood volume absorbs a larger amount of light, the origin of the signal modulation in reflective mode, however, is not entirely clear and still subject to research [20,30]. Moreover, there is a lot of confusion about the direction of the original, raw signal's course.…”

Section: Photoplethysmographymentioning

confidence: 99%

The quest for raw signals

Wolling

Laerhoven

2020

Proceedings of the Third Workshop on Data: Acquisition to Analysis

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Photoplethysmography is an optical measurement principle which is present in most modern wearable devices such as fitness trackers and smartwatches. As the analysis of physiological signals requires reliable but energy-efficient algorithms, suitable datasets are essential for their development, evaluation, and benchmark. A broad variety of clinical datasets is available with recordings from medical pulse oximeters which traditionally apply transmission mode photoplethysmography at the fingertip or earlobe. However, only few publicly available datasets utilize recent reflective mode sensors which are typically worn at the wrist and whose signals show different characteristics. Moreover, the recordings are often advertised as raw, but then turn out to be preprocessed and filtered while the applied parameters are not stated. In this way, the heart rate and its variability can be extracted, but interesting secondary information from the non-stationary signal is often lost. Consequently, the test of novel signal processing approaches for wearable devices usually implies the gathering of own or the use of inappropriate data. In this paper, we present a multi-varied method to analyze the suitability and applicability of presumably raw photoplethysmography signals. We present an analytical tool which applies 7 decision metrics to characterize 10 publicly available datasets with a focus on less or ideally unfiltered, raw signals. Besides the review, we finally provide a guideline for future datasets, to suit to and to be applicable in digital signal processing, to support the development and evaluation of algorithms for resource-limited wearable devices. CCS CONCEPTS • Human-centered computing → Ubiquitous and mobile devices; • Hardware → Digital signal processing; • Applied computing → Health informatics.

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Dynamics Reconstruction of Remote Photoplethysmography

Alam

et al. 2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Photoplethysmogram at green light: Where does chaos arise from?

Cited by 43 publications

References 38 publications

A Systematic Literature Review of the Factors that Influence the Accuracy of Consumer Wearable Health Device Data

A Systematic Literature Review of the Factors that Influence the Accuracy of Consumer Wearable Health Device Data

The quest for raw signals

Dynamics Reconstruction of Remote Photoplethysmography

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