Effective PPG sensor placement for reflected red and green light, and infrared wristband-type photoplethysmography

Lee, Seungmin; Shin, Hyunsoon; Hahm, ChanYoung

doi:10.1109/icact.2016.7423469

Cited by 10 publications

(6 citation statements)

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“…For EDA, 1‐minute data segments with <4 seconds of artifact were labeled good quality. Marginal‐quality data segments had both tonic and phasic EDA components, but each segment contained at least 4 seconds of artifacts (sharp changes 17 ). The Empatica E4 device returns values <0.05 µS when not in contact with skin (for example, with a garment under the device), and zero when entirely off the subject.…”

Section: Methodsmentioning

confidence: 99%

“…To minimize reviewer burden, the IR and green channels for Byteflies and Biovotion devices, respectively, were scored by the reviewers, and these scores were applied to all three PPG signals to calculate SQIs. The IR PPG signal for the Byteflies device and green PPG for the Biovotion device each showed the greatest physiological signal component on visual inspection 17–19 . The Empatica does not provide raw PPG values but rather provides blood volume pulse (BVP), a derived signal calculated from the red and green raw PPG signals.…”

Section: Methodsmentioning

confidence: 99%

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Signal quality and patient experience with wearable devices for epilepsy management

et al. 2020

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Noninvasive wearable devices have great potential to aid the management of epilepsy, but these devices must have robust signal quality, and patients must be willing to wear them for long periods of time. Automated machine learning classification of wearable biosensor signals requires quantitative measures of signal quality to automatically reject poor‐quality or corrupt data segments. In this study, commercially available wearable sensors were placed on patients with epilepsy undergoing in‐hospital or in‐home electroencephalographic (EEG) monitoring, and healthy volunteers. Empatica E4 and Biovotion Everion were used to record accelerometry (ACC), photoplethysmography (PPG), and electrodermal activity (EDA). Byteflies Sensor Dots were used to record ACC and PPG, the Activinsights GENEActiv watch to record ACC, and Epitel Epilog to record EEG data. PPG and EDA signals were recorded for multiple days, then epochs of high‐quality, marginal‐quality, or poor‐quality data were visually identified by reviewers, and reviewer annotations were compared to automated signal quality measures. For ACC, the ratio of spectral power from 0.8 to 5 Hz to broadband power was used to separate good‐quality signals from noise. For EDA, the rate of amplitude change and prevalence of sharp peaks significantly differentiated between good‐quality data and noise. Spectral entropy was used to assess PPG and showed significant differences between good‐, marginal‐, and poor‐quality signals. EEG data were evaluated using methods to identify a spectral noise cutoff frequency. Patients were asked to rate the usability and comfort of each device in several categories. Patients showed a significant preference for the wrist‐worn devices, and the Empatica E4 device was preferred most often. Current wearable devices can provide high‐quality data and are acceptable for routine use, but continued development is needed to improve data quality, consistency, and management, as well as acceptability to patients.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Signal quality and patient experience with wearable devices for epilepsy management

et al. 2020

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“…However, limitations of other electronic devices using red light to extract information from oxy-and deoxyhemoglobin in tissue, or for PPG, are now better understood. The major issue comes from motion artifacts, even small, disrupting the light transmission between the light emitter and captor [31], which explains why major brands switched from red to green light for PPG, especially in the wrist area, also less vascularized than other body sites [29,32,33]; hence, manufacturer's instructions require the subject to be totally still during the SpO 2 measuring process, to avoid any motion artifacts. Another reason may rely on the mixed arterial and venous blood through which the light passes, in the wrist area.…”

Section: Discussionmentioning

confidence: 99%

Accuracy and Reliability of Pulse O2 Saturation Measured by a Wrist-worn Oximeter

et al. 2021

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This study aims to evaluate the accuracy of the Garmin Forerunner 245 heart rate (HR) and pulse O2 saturation (SpO2) sensors compared with electrocardiogram and medical oximeter, from sea level to high altitude. Ten healthy subjects underwent five tests in normoxia and hypoxia (simulated altitudes from 3000 to 5500 m), consisting in a 5-min rest phase, followed by 5-min of mild exercise. Absolute error (±10 bpm for HR and ±3% for SpO2, around criterion) and intraclass correlations (ICC) were calculated. Error rates for HR remained under 10%, except at 3000 m, and ICCs evidenced a good reliability between Garmin and criterion. Overall SpO2 was higher than criterion (P<0.001) with a >50% error rate (>80% above 4800 m), and a poor reliability with criterion. The Garmin device displayed acceptable HR data at rest and exercise for all altitudes, but failed to provide trustworthy SpO2 values, especially at high altitude, where a pronounced arterial O2 desaturation could lead to acute mountain sickness in hypoxia-sensitive subjects, and its life-threatening complications; moreover, readings of overestimated SpO2 values might induce trekkers into further hazardous behavior by pursuing an ascent while being already at risk. Therefore, its use to assess SpO2 should be proscribed in altitude for acclimatization evaluation.

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“…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%

“…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]. In addition, rPPG light is not absorbed by melanin (the pigmentation that is responsible for the colour of the human skin) but gPPG light absorbs melanin.…”

Section: The Tracker and Sensor Typesmentioning

confidence: 99%

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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Effective PPG sensor placement for reflected red and green light, and infrared wristband-type photoplethysmography

Cited by 10 publications

References 0 publications

Signal quality and patient experience with wearable devices for epilepsy management

Signal quality and patient experience with wearable devices for epilepsy management

Accuracy and Reliability of Pulse O2 Saturation Measured by a Wrist-worn Oximeter

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

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