Abstract
Background
Compensatory
movements are commonly employed by stroke survivors during seated
reaching and may have negative effects on their long-term recovery.
Detecting compensation is useful for coaching the patient to reduce
compensatory trunk movements and improving the motor function of the
paretic arm. Sensor-based and camera-based systems have been developed
to detect compensatory movements, but they still have some limitations,
such as causing object obstructions, requiring complex setups and
raising privacy concerns. To overcome these drawbacks, this paper
proposes a compensatory movement detection system based on pressure
distribution data and is unobtrusive, simple and practical. Machine
learning algorithms were applied to classify compensatory movements
automatically. Therefore, the purpose of this study was to develop and
test a pressure distribution-based system for the automatic detection of
compensation movements of stroke survivors using machine learning
algorithms.
Methods
Eight
stroke survivors performed three types of reaching tasks
(back-and-forth, side-to-side, and up-and-down reaching tasks) with both
the healthy side and the affected side. The pressure distribution data
were recorded, and five features were extracted for classification. The k-nearest neighbor (k-NN)
and support vector machine (SVM) algorithms were applied to detect and
categorize the compensatory movements. The surface electromyography
(sEMG) signals of nine trunk muscles were acquired to provide a detailed
description and explanation of compensatory movements.
Results
Cross-validation yielded high classification accuracies (F1-score>0.95) for both the k-NN
and SVM classifiers in detecting compensation movements during all the
reaching tasks. In detail, an excellent performance was achieved in
discriminating between compensation and noncompensation (NC) movements,
with an average F1-score of 0.993. For the multiclass classification of
compensatory movement patterns, an average F1-score of 0.981 was
achieved in recognizing the NC, trunk lean-forward (TLF), trunk rotation
(TR) and shoulder elevation (SE) movements.
Conclusions
Good
classification performance in detecting and categorizing compensatory
movements validated the feasibility of the proposed pressure
distribution-based system. Reliable classification accuracy achieved by
the machine learning algorithms indicated the potential to monitor
compensation movements automatically by using the pressure
distribution-based system when stroke survivors perform seated reaching
tasks.
No comments:
Post a Comment