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Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning...

Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_3ee79f1ef8b14e56a0949c5cbba0c61e

Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study

About this item

Full title

Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study

Publisher

Switzerland: MDPI AG

Journal title

Sensors (Basel, Switzerland), 2024-11, Vol.24 (22), p.7258

Language

English

Formats

Publication information

Publisher

Switzerland: MDPI AG

More information

Scope and Contents

Contents

Background: Three-dimensional gait analysis, supported by advanced sensor systems, is a crucial component in the rehabilitation assessment of post-stroke hemiplegic patients. However, the sensor data generated from such analyses are often complex and challenging to interpret in clinical practice, requiring significant time and complicated procedure...

Alternative Titles

Full title

Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_3ee79f1ef8b14e56a0949c5cbba0c61e

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_3ee79f1ef8b14e56a0949c5cbba0c61e

Other Identifiers

ISSN

1424-8220

E-ISSN

1424-8220

DOI

10.3390/s24227258

How to access this item