Evidence map›Paper›PMID 37591885›Full record

ArticleScientific reports2023

Multispectral sensor fusion in SmartWatch for in situ continuous monitoring of human skin hydration and body sweat loss.

Elena Volkova, Alexey Perchik, Konstantin Pavlov, Evgenii Nikolaev, Alexey Ayuev, Jaehyuck Park, Namseok Chang, Wonseok Lee, Justin Younghyun Kim, Alexander Doronin and 1 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 18 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 3 countries.

Elena VolkovaSensor Solutions Laboratory, Samsung R&D Institute Russia, 127018, Moscow, Russia. v.elena@samsung.com.
Alexey PerchikSensor Solutions Laboratory, Samsung R&D Institute Russia, 127018, Moscow, Russia.
Konstantin PavlovSensor Solutions Laboratory, Samsung R&D Institute Russia, 127018, Moscow, Russia.
Evgenii NikolaevSensor Solutions Laboratory, Samsung R&D Institute Russia, 127018, Moscow, Russia.
Alexey AyuevSensor Solutions Laboratory, Samsung R&D Institute Russia, 127018, Moscow, Russia.
Jaehyuck ParkHealth H/W R&D Group, Samsung Electronics, Suwon, 16678, Korea.
Namseok ChangHealth H/W R&D Group, Samsung Electronics, Suwon, 16678, Korea.
Wonseok LeeHealth H/W R&D Group, Samsung Electronics, Suwon, 16678, Korea.
Justin Younghyun KimHealth H/W R&D Group, Samsung Electronics, Suwon, 16678, Korea.
Alexander DoroninSchool of Engineering and Computer Science, Victoria University of Wellington, 6140, Wellington, New Zealand.
Maksim VilenskiiSensor Solutions Laboratory, Samsung R&D Institute Russia, 127018, Moscow, Russia.
Samsung (Russia) · RUSamsung (South Korea) · KRVictoria University of Wellington · NZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Post-pandemic health operations have become a near-term reality, discussions around wearables are on the rise. How do wearable health solutions effectively deploy and use this opportunity to fill the gap between wellness and healthcare? In this paper, we will talk about wearable healthcare diagnosis, with a particular focus on monitoring skin hydration using optical multi-wavelength sensor fusion. Continuous monitoring of human skin hydration is a task of paramount importance for maintaining water loss dynamics for fitness lovers as well as for skin beauty, integrity and the health of the entire body. Preserving the appropriate levels of hydration ensures consistency of weight, positively affects psychological state, and proven to result in a decrease in blood pressure as well as the levels of "bad" cholesterol while slowing down the aging processes. Traditional methods for determining the state of water content in the skin do not allow continuous and non-invasive monitoring, which is required for variety of consumer, clinical and cosmetic applications. We present novel sensing technology and a pipeline for capturing, modeling and analysis of the skin hydration phenomena and associated changes therein. By expanding sensing capabilities built into the SmartWatch sensor and combining them with advanced modeling and Machine Learning (ML) algorithms, we identified several important characteristics of photoplethysmography (PPG) signal and spectral sensitivity corresponding to dynamics of skin water content. In a hardware aspect, we newly propose the expansion of SmartWatch capabilities with InfraRed light sources equipped with wavelengths of 970 nm and 1450 nm. Evaluation of the accuracy and characteristics of PPG sensors has been performed with biomedical optics-based simulation framework using Monte Carlo simulations. We performed rigorous validation of the developed technology using experimental and clinical studies. The developed pipeline serves as a tool in the ongoing studies of the next generation of optical sensing technology.

Indexed as

SkinSweatAlgorithmsBlood PressureComputer SimulationHumans

Identifiers

PMID37591885
PMCPMC10435441
OpenAlexW4385954500

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.