Evidence map›Paper›PMID 36590154›Full record

ReviewFrontiers in medical technology2022

Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies.

Abdul Aziz Hulleck, Dhanya Menoth Mohan, Nada Abdallah, Marwan El Rich, Kinda Khalaf

Registry-linked trialOpen access · goldAbstract readReview
In one paragraph

Review in Frontiers in medical technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07572214 (Is Markerless Three-dimensional Motion Analysis a Viable Alternative to Traditional Gait Analysis?), which is not on this map. Cited by 126 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
126citing papers in PubMed, 8 pooled it
86.8field-weighted citation impact, top 1% 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.

NCT07572214 completednot on this mapstarted 2025, after this paper: background citation

Is Markerless Three-dimensional Motion Analysis a Viable Alternative to Traditional Gait Analysis? (IMAGine Study)

TypeobservationalSponsorAlder Hey Children's NHS Foundation TrustRan2025 to 2025Enrolled100ConditionsPaediatric Patients
3 · Its place in the literature

Who cites it

126 citing papers in PubMed, 8 syntheses or guidelines pooled it, 222 citations in OpenAlex.

  1. Pooled it
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  4. Gait analysis methods in people with spinal cord injury: a systematic review.Journal of neuroengineering and rehabilitation · 2026
    Pooled it
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  18. Step-by-step insight into gait analysis: A narrative review unlocking knee biomechanics.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026
    Review
  19. Review
  20. Article

66 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

5 authors at 3 institutions in 3 countries.

Abdul Aziz HulleckMechanical Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates.
Dhanya Menoth MohanSchool of Mechanical and Aerospace Engineering, Monash University, Clayton Campus, Melbourne, Australia.
Nada AbdallahWeill Cornell Medicine, New York City, NY, United States.
Marwan El RichMechanical Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates.
Kinda KhalafBiomedical Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates.
Khalifa University of Science and Technology · AEMonash University · AUWeill Cornell Medicine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Despite being available for more than three decades, quantitative gait analysis remains largely associated with research institutions and not well leveraged in clinical settings. This is mostly due to the high cost/cumbersome equipment and complex protocols and data management/analysis associated with traditional gait labs, as well as the diverse training/experience and preference of clinical teams. Observational gait and qualitative scales continue to be predominantly used in clinics despite evidence of less efficacy of quantifying gait. Research objective: This study provides a scoping review of the status of clinical gait assessment, including shedding light on common gait pathologies, clinical parameters, indices, and scales. We also highlight novel state-of-the-art gait characterization and analysis approaches and the integration of commercially available wearable tools and technology and AI-driven computational platforms. Methods: A comprehensive literature search was conducted within PubMed, Web of Science, Medline, and ScienceDirect for all articles published until December 2021 using a set of keywords, including normal and pathological gait, gait parameters, gait assessment, gait analysis, wearable systems, inertial measurement units, accelerometer, gyroscope, magnetometer, insole sensors, electromyography sensors. Original articles that met the selection criteria were included. Results and significance: Clinical gait analysis remains highly observational and is hence subjective and largely influenced by the observer's background and experience. Quantitative Instrumented gait analysis (IGA) has the capability of providing clinicians with accurate and reliable gait data for diagnosis and monitoring but is limited in clinical applicability mainly due to logistics. Rapidly emerging smart wearable technology, multi-modality, and sensor fusion approaches, as well as AI-driven computational platforms are increasingly commanding greater attention in gait assessment. These tools promise a paradigm shift in the quantification of gait in the clinic and beyond. On the other hand, standardization of clinical protocols and ensuring their feasibility to map the complex features of human gait and represent them meaningfully remain critical challenges.

Indexed as

clinical gait assessmentgait measuresgait pathologiesgait technologiesmobile gait lab

Identifiers

PMID36590154
PMCPMC9800936
OpenAlexW4313275909

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.