Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patient...
Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients
About this item
Full title
Author / Creator
Publisher
Canada: Gunther Eysenbach MD MPH, Associate Professor
Journal title
Language
English
Formats
Publication information
Publisher
Canada: Gunther Eysenbach MD MPH, Associate Professor
Subjects
More information
Scope and Contents
Contents
The pronator drift test (PDT), a neurological examination, is widely used in clinics to measure motor weakness of stroke patients.
The aim of this study was to develop a PDT tool with machine learning classifiers to detect stroke symptoms based on quantification of proximal arm weakness using inertial sensors and signal processing.
We extract...
Alternative Titles
Full title
Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5413803
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5413803
Other Identifiers
ISSN
1439-4456
E-ISSN
1438-8871
DOI
10.2196/jmir.7092
