  The generalizability of machine learning (ML) models for wearable monitoring
in stroke rehabilitation is often constrained by the limited scale and
heterogeneity of available data. Data augmentation addresses this challenge by
adding computationally derived data to real data to enrich the variability
represented in the training set. Traditional augmentation methods, such as
rotation, permutation, and time-warping, have shown some benefits in improving
classifier performance, but often fail to produce realistic training examples.
This study employs Conditional Generative Adversarial Networks (cGANs) to
create synthetic kinematic data from a publicly available dataset, closely
mimicking the experimentally measured reaching movements of stroke survivors.
This approach not only captures the complex temporal dynamics and common
movement patterns after stroke, but also significantly enhances the training
dataset. By training deep learning models on both synthetic and experimental
data, we achieved a substantial enhancement in task classification accuracy:
models incorporating synthetic data attained an overall accuracy of 80.2%,
significantly higher than the 63.1% seen in models trained solely with real
data. These improvements allow for more precise task classification, offering
clinicians the potential to monitor patient progress more accurately and tailor
rehabilitation interventions more effectively.
