DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing.

Qichang Zhang, Qing Wang, Weimin Lyu, Changyuan Yu

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

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

1 citing paper in PubMed.

SMART MAT: Fibre Optic Innovation for Bedside Monitoring and Validation of Continuous Vital Signs.Sensors (Basel, Switzerland) · 2025
Article

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

4 authors.

Qichang ZhangDepartment of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong.

Qing WangDepartment of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong.ORCID 0000-0002-3396-4805

Weimin LyuDepartment of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong.ORCID 0000-0003-4991-5466

Changyuan YuDepartment of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong.ORCID 0000-0002-3185-0441

Funding

Non-wearable and non-invasive photonic sleep monitoring system based on optical fiber sensor with machine learning HKPU 1-WZ01Shenzhen-Hong Kong-Macao Science and Technology Plan C SGDX 2020110309520303 K-ZGCQ

6 · The paper itself

Abstract

Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach-Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.

Indexed as

Deep LearningOptical FibersVital SignsHumansMonitoring, PhysiologicNeural Networks, ComputerDEMAEMDLSTMMZIoptical fiber sensorvital signs monitoring

Identifiers

PMID38732777

DOI10.3390/s24092672

PMCPMC11085620

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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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.