You do realize assessments are totally fucking useless in getting survivors recovered? Obviously not; then get the hell out of stroke and try something easier!
aBnormal motION capture In aCute Stroke (BIONICS): A Low-Cost Tele-Evaluation Tool for Automated Assessment of Upper Extremity Function in Stroke Patients
Abstract
Background
The
incidence of stroke and stroke-related hemiparesis has been steadily
increasing and is projected to become a serious social, financial, and
physical burden on the aging population. Limited access to outpatient
rehabilitation for these stroke survivors further deepens the healthcare
issue and estranges the stroke patient demographic in rural areas.
However, new advances in motion detection deep learning enable the use
of handheld smartphone cameras for body tracking, offering unparalleled
levels of accessibility.
Methods
In
this study we want to develop an automated method for evaluation of a
shortened variant of the Fugl-Meyer assessment, the standard stroke
rehabilitation scale describing upper extremity motor function. We pair
this technology with a series of machine learning models, including
different neural network structures and an eXtreme Gradient Boosting
model, to score 16 of 33 (49%) Fugl-Meyer item activities.
Results
In
this observational study, 45 acute stroke patients completed at least 1
recorded Fugl-Meyer assessment for the training of the auto-scorers,
which yielded average accuracies ranging from 78.1% to 82.7% item-wise.
Conclusion
In
this study, an automated method was developed for the evaluation of a
shortened variant of the Fugl-Meyer assessment, the standard stroke
rehabilitation scale describing upper extremity motor function. This
novel method is demonstrated with potential to conduct telehealth
rehabilitation evaluations and assessments with accuracy and
availability.
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