Evidence map›Paper›PMID 36501804›Full record

ReviewSensors (Basel, Switzerland)2022

Lower Limb Exoskeleton Sensors: State-of-the-Art.

Slávka Neťuková, Martin Bejtic, Christiane Malá, Lucie Horáková, Patrik Kutílek, Jan Kauler, Radim Krupička

Open access · goldAbstract readReview
In one paragraph

Review 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 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Review on Portable-Powered Lower Limb Exoskeletons.Sensors (Basel, Switzerland) · 2024
    Review
  8. Review
  9. Review
  10. Article
  11. Review
  12. Review
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

7 authors at 1 institution in 1 country.

Slávka NeťukováFaculty of Biomedical Engineering, Czech Technical University in Prague, 272 01 Kladno, Czech Republic.ORCID 0000-0002-0383-8787
Martin BejticFaculty of Biomedical Engineering, Czech Technical University in Prague, 272 01 Kladno, Czech Republic.
Christiane MaláFaculty of Biomedical Engineering, Czech Technical University in Prague, 272 01 Kladno, Czech Republic.ORCID 0000-0001-5440-8712
Lucie HorákováFaculty of Biomedical Engineering, Czech Technical University in Prague, 272 01 Kladno, Czech Republic.
Patrik KutílekFaculty of Biomedical Engineering, Czech Technical University in Prague, 272 01 Kladno, Czech Republic.
Jan KaulerFaculty of Biomedical Engineering, Czech Technical University in Prague, 272 01 Kladno, Czech Republic.
Radim KrupičkaFaculty of Biomedical Engineering, Czech Technical University in Prague, 272 01 Kladno, Czech Republic.ORCID 0000-0002-0280-215X
Czech Technical University in Prague · CZ

Funding

Ministry of Health of the Czech Republic NU20 04 00327the project National Institute for Neurological Research (Programme EXCELES) - Funded by the European Union - Next Generation EU LX22NPO5107
6 · The paper itself

Abstract

Due to the ever-increasing proportion of older people in the total population and the growing awareness of the importance of protecting workers against physical overload during long-time hard work, the idea of supporting exoskeletons progressed from high-tech fiction to almost commercialized products within the last six decades. Sensors, as part of the perception layer, play a crucial role in enhancing the functionality of exoskeletons by providing as accurate real-time data as possible to generate reliable input data for the control layer. The result of the processed sensor data is the information about current limb position, movement intension, and needed support. With the help of this review article, we want to clarify which criteria for sensors used in exoskeletons are important and how standard sensor types, such as kinematic and kinetic sensors, are used in lower limb exoskeletons. We also want to outline the possibilities and limitations of special medical signal sensors detecting, e.g., brain or muscle signals to improve data perception at the human-machine interface. A topic-based literature and product research was done to gain the best possible overview of the newest developments, research results, and products in the field. The paper provides an extensive overview of sensor criteria that need to be considered for the use of sensors in exoskeletons, as well as a collection of sensors and their placement used in current exoskeleton products. Additionally, the article points out several types of sensors detecting physiological or environmental signals that might be beneficial for future exoskeleton developments.

Indexed as

Exoskeleton DeviceAgedBiomechanical PhenomenaHumansLower ExtremityMovementexoskeletonslower limbspowered orthosissensorswearable robots

Identifiers

PMID36501804
PMCPMC9738474
OpenAlexW4310036636

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.