Evidence map›Paper›PMID 35271088›Full record

ArticleSensors (Basel, Switzerland)2022

A Wearable, Multi-Frequency Device to Measure Muscle Activity Combining Simultaneous Electromyography and Electrical Impedance Myography.

Chuong Ngo, Carlos Munoz, Markus Lueken, Alfred Hülkenberg, Cornelius Bollheimer, Andrey Briko, Alexander Kobelev, Sergey Shchukin, Steffen Leonhardt

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.3field-weighted citation impact, top 12% 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

16 citing papers in PubMed, 33 citations in OpenAlex.

  1. Trial
  2. Review
  3. Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  4. Article
  5. Review
  6. Wearable Bioelectronics for Home-Based Monitoring and Treatment of Muscle Atrophy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  7. Article
  8. Article
  9. Review
  10. An Embedded Electromyogram Signal Acquisition Device.Sensors (Basel, Switzerland) · 2024
    Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Review
  16. 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

9 authors at 3 institutions in 2 countries.

Chuong NgoMedical Information Technology, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany.ORCID 0000-0002-3961-2173
Carlos MunozMedical Information Technology, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany.
Markus LuekenMedical Information Technology, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany.ORCID 0000-0002-2487-0962
Alfred HülkenbergMedical Information Technology, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany.
Cornelius BollheimerDepartment of Geriatrics, University Hospital Aachen, 52074 Aachen, Germany.
Andrey BrikoDepartment of Medical and Technical Information Technology, Bauman Moscow State Technical University, 105005 Moscow, Russia.
Alexander KobelevDepartment of Medical and Technical Information Technology, Bauman Moscow State Technical University, 105005 Moscow, Russia.ORCID 0000-0001-6715-7668
Sergey ShchukinDepartment of Medical and Technical Information Technology, Bauman Moscow State Technical University, 105005 Moscow, Russia.
Steffen LeonhardtMedical Information Technology, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany.ORCID 0000-0002-6898-6887
RWTH Aachen University · DEBauman Moscow State Technical University · RUUniversitätsklinikum Aachen · DE

Funding

Deutsche Forschungsgemeinschaft LE 817/41-1Russian Foundation for Basic Research 20-58-12006
6 · The paper itself

Abstract

The detection of muscle contraction and the estimation of muscle force are essential tasks in robot-assisted rehabilitation systems. The most commonly used method to investigate muscle contraction is surface electromyography (EMG), which, however, shows considerable disadvantages in predicting the muscle force, since unpredictable factors may influence the detected force but not necessarily the EMG data. Electrical impedance myography (EIM) investigates the change in electrical impedance during muscle activities and is another promising technique to investigate muscle functions. This paper introduces the design, development, and evaluation of a device that performs EMG and EIM simultaneously for more robust measurement of muscle conditions subject to artifacts. The device is light, wearable, and wireless and has a modular design, in which the EMG, EIM, micro-controller, and communication modules are stacked and interconnected through connectors. As a result, the EIM module measures the bioimpedance between 20 and 200 Ω with an error of less than 5% at 140 SPS. The settling time during the calibration phase of this module is less than 1000 ms. The EMG module captures the spectrum of the EMG signal between 20-150 Hz at 1 kSPS with an SNR of 67 dB. The micro-controller and communication module builds an ARM-Cortex M3 micro-controller which reads and transfers the captured data every 1 ms over RF (868 Mhz) with a baud rate of 500 kbps to a receptor connected to a PC. Preliminary measurements on a volunteer during leg extension, walking, and sit-to-stand showed the potential of the system to investigate muscle function by combining simultaneous EMG and EIM.

Indexed as

Muscle ContractionWearable Electronic DevicesElectric ImpedanceElectromyographyHumansMusclesbioimpedanceelectrical impedance myographyelectromyogramhardware designmulti-frequencymuscle forcesensor fusionwearables

Identifiers

PMID35271088
PMCPMC8914780
OpenAlexW4214898985

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.