Evidence map›Paper›PMID 42415976›Full record

ReviewNeurosciences (Riyadh, Saudi Arabia)2026

Wearable Sensor Technology and Biomechanical Gait Analysis in Post-Stroke Rehabilitation: A Review with Implications for the Saudi Healthcare System.

Abdullah H Alzahrani

Abstract readReview
In one paragraph

Review in Neurosciences (Riyadh, Saudi Arabia), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Abdullah H AlzahraniDepartment of Health Rehabilitation, College of Applied Medical Sciences at Shaqra, Shaqra University, Shaqra, Kingdom of Saudi Arabia.ORCID 0000-0002-4252-8255

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stroke remains a major cause of disability worldwide, with gait impairment affecting up to 80% of survivors and contributing to long-term dependence, fall risk, and reduced quality of life. In Saudi Arabia, stroke incidence is rising, with a significant burden on younger populations and disparities in access to post-stroke rehabilitation services. Conventional gait analysis tools rely on expensive, specialized laboratory equipment, which limits their availability in low-resource and rural settings. Recent advances in wearable sensor technologies, including inertial measurement units, pressure insoles, and electromyography systems, offer portable, scalable, and cost-efficient options for assessing gait in real-world environments. These systems can offer continuous monitoring, tele-rehabilitation, and personalized feedback, aligning with global shifts toward digital health oversight. This review explores the application of wearable sensor-based gait analysis in post-stroke rehabilitation, highlighting sensor modalities, clinical utility, implementation challenges, and opportunities, particularly in Saudi Arabia, to strengthen rehabilitation services and optimize patient outcomes.

Indexed as

GaitGait AnalysisStrokeStroke RehabilitationWearable Electronic DevicesBiomechanical PhenomenaDigital HealthHumansSaudi ArabiaBiomechanicsGait analysisSaudi ArabiaStroke rehabilitationWearable sensors

Identifiers

PMID42415976
PMCPMC13340605

What OpenQuestion holds

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LicenceCC BY-NC
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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.