Evidence map›Paper›PMID 33924403›Full record

SynthesisSensors (Basel, Switzerland)2021

Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review.

Hari Prasanth, Miroslav Caban, Urs Keller, Grégoire Courtine, Auke Ijspeert, Heike Vallery, Joachim von Zitzewitz

Abstract readSystematic Review
In one paragraph

Synthesis 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 159 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
159citing papers in PubMed, 8 pooled it
–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

159 citing papers in PubMed, 8 syntheses or guidelines pooled it.

  1. Pooled it
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  9. Trial
  10. Review
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  14. Observational
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  17. Review
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  19. Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026
    Review
  20. Article

99 more citing papers are in PubMed but not listed here.

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.

Hari PrasanthONWARD, Building 32, Hightech Campus, 5656 AE Eindhoven, The Netherlands.
Miroslav CabanInstitute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.ORCID 0000-0002-2627-4291
Urs KellerONWARD, EPFL Innovation Park Building C, 1015 Lausanne, Switzerland.
Grégoire CourtineCenter for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), 1015 Lausanne, Switzerland.
Auke IjspeertInstitute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
Heike ValleryFaculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands.
Joachim von ZitzewitzONWARD, EPFL Innovation Park Building C, 1015 Lausanne, Switzerland.ORCID 0000-0002-4662-9088

Funding

Horizon 2020 Framework Programme 779963
6 · The paper itself

Abstract

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.

Indexed as

Activities of Daily LivingWearable Electronic DevicesBiomechanical PhenomenaGaitGait AnalysisHumansassistive devicegait analysisgait rehabilitationinertial measurement unitinsole pressure sensorspathological gaitreal-time gait detectionwearable sensor

Identifiers

PMID33924403
PMCPMC8069962

What OpenQuestion holds

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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.