Evidence map›Paper›PMID 35009644›Full record

ArticleSensors (Basel, Switzerland)2021

A Real-Time Wearable Physiological Monitoring System for Home-Based Healthcare Applications.

Jin-Woo Jeong, Woochan Lee, Young-Joon Kim

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.0field-weighted citation impact, top 8% 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

9 citing papers in PubMed, 57 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. A Review on Assisted Living Using Wearable Devices.Sensors (Basel, Switzerland) · 2024
    Review
  7. Article
  8. Review
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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

3 authors at 2 institutions in 1 country.

Jin-Woo JeongDepartment of Electronic Engineering, Gachon University, Seongnam 13120, Korea.
Woochan LeeDepartment of Electrical Engineering, Incheon National University, Incheon 22012, Korea.ORCID 0000-0001-6726-2599
Young-Joon KimDepartment of Electronic Engineering, Gachon University, Seongnam 13120, Korea.ORCID 0000-0003-3511-3438
Gachon University · KRIncheon National University · KR

Funding

National Research Foundation of Korea NRF-2019R1C1C1011235National Research Foundation of Korea NRF-2020M3A9E4104385
6 · The paper itself

Abstract

The acquisition of physiological data are essential to efficiently predict and treat cardiac patients before a heart attack occurs and effectively expedite motor recovery after a stroke. This goal can be achieved by using wearable wireless sensor network platforms for real-time healthcare monitoring. In this paper, we present a wireless physiological signal acquisition device and a smartphone-based software platform for real-time data processing and monitor and cloud server access for everyday ECG/EMG signal monitoring. The device is implemented in a compact size (diameter: 30 mm, thickness: 4.5 mm) where the biopotential is measured and wirelessly transmitted to a smartphone or a laptop for real-time monitoring, data recording and analysis. Adaptive digital filtering is applied to eliminate any interference noise that can occur during a regular at-home environment, while minimizing the data process time. The accuracy of ECG and EMG signal coverage is assessed using Bland-Altman analysis by comparing with a reference physiological signal acquisition instrument (RHS2116 Stim/Recording System, Intan). Signal coverage of R-R peak intervals showed almost identical outcome between this proposed work and the RHS2116, showing a mean difference in heart rate of 0.15 ± 4.65 bpm and a Wilcoxon's

Indexed as

Wearable Electronic DevicesDelivery of Health CareElectrocardiographyHome EnvironmentHumansMonitoring, PhysiologicSignal Processing, Computer-AssistedECG/EMG sensingphysiological monitorrehabilitation trainingsmart wearable devicewireless communication

Identifiers

PMID35009644
PMCPMC8747365
OpenAlexW4200572332

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.