JMIR Publications

ABSTRACT

Background:

Measuring the use of an affected limb in a home setting after hospital discharge is crucial for stroke rehabilitation. Classifying movements using non-intrusive wearable sensor data provides a context for arm use, and it is essential for the development of a home rehabilitation system with monitoring and feedback functions. However, classification of the movements of stroke patients poses some challenges, including variability in duration and patterns, as well as data sparsity.

Objective:

We aimed to investigate methods for the preprocessing, training, and evaluation of a deep learning model to effectively classify the movements of stroke patients with hemiparesis. To address the abovementioned challenges, we adopted linear interpolation in preprocessing, compared the model performance using different training groups (non-disabled individuals and patients with stroke), tested the effect of data augmentation, and observed how the asymmetry of movements affected the model performance.

Methods:

Fifteen patients with subacute to chronic stroke (Stroke group) and 29 non-disabled individuals (ND group) participated in two different tasks, a range of motion (ROM; 14 movements) task and activities of daily living (ADL; 56 movements) task, wearing five inertial measurement unit sensors in a home setting. We trained a one-dimensional convolutional neural network (1D-CNN) and evaluated its performance using the F1-score for different training conditions, including ND only, Stroke only, and combined ND+Stroke. In addition, we tested the effect of axis rotation as a data augmentation technique. The model performance was further investigated with different asymmetricities of movement.

Results:

Training with combined ND+Stroke data demonstrated the best results (ROM, F1=0.721; ADL, F1=0.603), followed by Stroke only (ROM, F1=0.676; ADL, F1=0.526) and ND only (ROM, F1=0.553; ADL, F1=0.454) for the original and augmented data. Axis rotation consistently boosted performance in all training conditions (ROM, P=.014; ADL, P<.001). For the Stroke group, bimanual symmetric movements showed significantly better performance (F1=0.757) than bimanual asymmetric (F1=0.541) and unimanual (F1=0.581) movements (P<.001) in the ADL task.

Conclusions:

Effectively training a deep learning model for the classification of the movements of stroke patients requires adjusting the data length to a fixed size. Despite the distinct and diverse patterns of stroke movements, it is still beneficial to train a model using combined ND+Stroke data. Proper data augmentation, such as axis rotation, further enhances the model performance. Finally, the result on stroke data was differently affected by the asymmetry of movements, with better performance for symmetric than asymmetric movements. These findings may provide the best practices and processes for classifying the movements of stroke patients when training and evaluating deep learning models.