Stroke therapy is essential to
reduce
impairments and improve the motor movements of stroke survivors, however
sessions can be expensive, time consuming, and geographically limited.
Robotic stroke therapy seeks to remedy the limitations of traditional
stroke therapy, but it is hampered by incorrect movements during the
session. Incorrect usage of muscles, called as compensatory movements,
can cause problems that can hamper the recovery of the patients.
(You mean you don't know that you learn faster by doing incorrect movements and then correcting them? We know that learning from your mistakes is one of the best ways to learn,) Thus,
there is a need to develop tools to automatically detect compensatory
movements to assist patients doing autonomous therapy sessions. Previous
studies on automatic detection using depth sensors did not yield
satisfactory results. This study explores class imbalance as a possible
reason for the low F1-score results on machine learning classifiers.
Different methods to address class imbalance were employed to improve
and to analyze the performance of the classifiers. The methods employed
allowed the classifiers to sometimes detect compensatory movements
however this degraded the performance of detecting the correct
movements. Adjusting the decision thresholds of outlier detection
algorithms shows this explicitly. Since addressing class imbalance only
marginally improves the performance of the classifiers, other possible
methods can be explored in conjunction with it. This study shows the
possibility of detecting compensations in stroke patients.
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