Smart Garment Fabrics to Enable Non-Contact Opto-Physiological Monitoring

Iakovlev, Dmitry; Hu, Sijung; Hassan, H.; Dwyer, Vincent M.; Ashayer-Soltani, Roya; Hunt, C; Shen, Jinsong

doi:10.3390/bios8020033

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…Eighteen articles published between the years 2016-2018 were included in the review [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] from conference or journal. Research originated from eleven different countries including Germany [9,10], Romania [11,12], India [13][14][15][16][17][18], Bangladesh [19], Iraq [20], Italy [21], Taiwan [22], Netherlands [23], Ecuador [24], Indonesia [25] and United Kingdom [26].…”

Section: Search Resultsmentioning

confidence: 99%

Wearable Sensor and Internet of Things Technology for Better Medical Science: A Review

Putri¹,

Ali²,

Saad³

et al. 2018

IJET

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E-health becomes one of the internet's products for healthcare. The problems of health service such as far hospital and expensive examination fees become the emergence of this technology. Consequently, people reluctant to check their health to hospital. E-health provides information on disease prevention, detecting early symptoms, and monitoring the patient's condition based on medical parameters from a far distance. Internet of things became the main concept in this system, which combines wearable sensors, communication systems, and mobile user interfaces. Reliable and valid system, easily carried, help the doctor to monitor patients from far distance expectantly to overcome the problems. The aims of this paper review are describing how an internet of things technology and wearable sensor help medical science and find the best way to create a health monitoring system.

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Section: Search Resultsmentioning

confidence: 99%

Wearable Sensor and Internet of Things Technology for Better Medical Science: A Review

Putri¹,

Ali²,

Saad³

et al. 2018

IJET

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show abstract

“…Figure 2 represents schematics detailing the configuration of the off-the-shelf PPG device. Based on the literature [26,[35][36][37][38][39], different LED and PD configurations have been assessed. Among these, the narrow-line-bottom configuration yielded the most favorable outcomes in terms of signal amplitude and the signal-to-noise ratio.…”

Section: Off-the-shelf Prototypementioning

confidence: 99%

A Guide to Measuring Heart and Respiratory Rates Based on Off-the-Shelf Photoplethysmographic Hardware and Open-Source Software

Stevens,

Hantson,

Larmuseau

et al. 2024

Sensors

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The remote monitoring of vital signs via wearable devices holds significant potential for alleviating the strain on hospital resources and elder-care facilities. Among the various techniques available, photoplethysmography stands out as particularly promising for assessing vital signs such as heart rate, respiratory rate, oxygen saturation, and blood pressure. Despite the efficacy of this method, many commercially available wearables, bearing Conformité Européenne marks and the approval of the Food and Drug Administration, are often integrated within proprietary, closed data ecosystems and are very expensive. In an effort to democratize access to affordable wearable devices, our research endeavored to develop an open-source photoplethysmographic sensor utilizing off-the-shelf hardware and open-source software components. The primary aim of this investigation was to ascertain whether the combination of off-the-shelf hardware components and open-source software yielded vital-sign measurements (specifically heart rate and respiratory rate) comparable to those obtained from more expensive, commercially endorsed medical devices. Conducted as a prospective, single-center study, the research involved the assessment of fifteen participants for three minutes in four distinct positions, supine, seated, standing, and walking in place. The sensor consisted of four PulseSensors measuring photoplethysmographic signals with green light in reflection mode. Subsequent signal processing utilized various open-source Python packages. The heart rate assessment involved the comparison of three distinct methodologies, while the respiratory rate analysis entailed the evaluation of fifteen different algorithmic combinations. For one-minute average heart rates’ determination, the Neurokit process pipeline achieved the best results in a seated position with a Spearman’s coefficient of 0.9 and a mean difference of 0.59 BPM. For the respiratory rate, the combined utilization of Neurokit and Charlton algorithms yielded the most favorable outcomes with a Spearman’s coefficient of 0.82 and a mean difference of 1.90 BrPM. This research found that off-the-shelf components are able to produce comparable results for heart and respiratory rates to those of commercial and approved medical wearables.

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“…Sensors could include ECG electrodes, photoplethysmography on limbs or the face (18) to detect pulse, or motion sensors (19) to detect lack of breathing or a sudden collapse. Sensors in smart houses (20), on smart mattresses (21) embedded in garment fabrics (22) or in other locations could network with portable devices to create an internet of things (IoT) for SCA. Although each independent data stream may be non-specific, their combination could be very effective particularly if personalized by adaptive tools including machine learning.…”

Section: Respondmentioning

confidence: 99%

New Concepts in Sudden Cardiac Arrest to Address an Intractable Epidemic

Narayan

Wang

Daubert

2019

Journal of the American College of Cardiology

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Sudden cardiac arrest (SCA) is one of the largest causes of mortality globally, with an out-of-hospital survival below 10 % despite intense research. This document outlines challenges in addressing the epidemic of SCA, along the framework of Respond, Understand and Predict, and Prevent. Response could be improved by technology-assisted orchestration of community responder systems, access to AEDs, and innovations to match resuscitation resources to victims in place and time. Efforts to understand and predict SCA may be enhanced by refining taxonomy along phenotypical and pathophysiological ‘axes of risk’, extending beyond cardiovascular pathology to identify less heterogeneous cohorts, facilitated by open-data platforms and analytics including machine learning to integrate discoveries across disciplines. Prevention of SCA must integrate these concepts, recognizing that all members of society are stakeholders. Ultimately, solutions to the public health challenge of SCA will require greater awareness, societal debate and focused public policy.

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Smart Garment Fabrics to Enable Non-Contact Opto-Physiological Monitoring

Cited by 9 publications

References 15 publications

Wearable Sensor and Internet of Things Technology for Better Medical Science: A Review

Wearable Sensor and Internet of Things Technology for Better Medical Science: A Review

A Guide to Measuring Heart and Respiratory Rates Based on Off-the-Shelf Photoplethysmographic Hardware and Open-Source Software

New Concepts in Sudden Cardiac Arrest to Address an Intractable Epidemic

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