Evidence map›Paper›PMID 42197950›Full record

ArticleSensors (Basel, Switzerland)2026

A Lightweight Machine Learning Framework for Post-Stroke Gait Abnormality Classification Using Wearable Gyroscope Features.

Stamatios Orfanos, Thanita Sanghan, Andreas Menychtas, Christos Panagopoulos, Ilias Maglogiannis, Surapong Chatpun

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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

6 authors.

Stamatios OrfanosBioassist SA, 26504 Rio, Greece.ORCID 0009-0005-5042-9408
Thanita SanghanDepartment of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand.ORCID 0009-0005-1693-5673
Andreas MenychtasDepartment of Digital Systems, Faculty of Information and Communication Technologies, University of Piraeus, 18534 Piraeus, Greece.ORCID 0000-0002-4510-5522
Christos PanagopoulosBioassist SA, 26504 Rio, Greece.ORCID 0000-0001-9282-0919
Ilias MaglogiannisDepartment of Digital Systems, Faculty of Information and Communication Technologies, University of Piraeus, 18534 Piraeus, Greece.ORCID 0000-0003-2860-399X
Surapong ChatpunDepartment of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand.ORCID 0000-0002-6888-5170

Funding

the Horizon Europe Framework Program 101086348
6 · The paper itself

Abstract

Accurately classifying gait abnormalities is crucial for the effective monitoring and rehabilitation of stroke patients. This study proposed a lightweight machine learning framework for distinguishing healthy from abnormal gait patterns using statistical features extracted from wearable gyroscope data. Statistical

Indexed as

GaitMachine LearningStrokeWearable Electronic DevicesClassification AlgorithmsHumansRandom ForestStroke RehabilitationSupport Vector Machinegait classificationgyroscope featuresmachine learningstroke rehabilitationwearable sensors

Identifiers

PMID42197950
PMCPMC13210589

What Socratic 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 Socratic graph.