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Figure 6. Confusion matrix of the four classification methods of upper extremity movements. CPN: Convolutional prototype network; LSTM:
long short-term memory; SVM: support vector machine.

Figure 7. Confusion matrix of the four classification methods of lower extremity movements. CPN: Convolutional prototype network; LSTM:
long short-term memory; SVM: support vector machine.

4. DISCUSSION
Motion analysis techniques offer a more objective and detailed view of the patient’s motor abilities, which can
detect subtle changes and nuances in movement that might not be noticeable to clinical observations. Previous
studies have combined motion analysis techniques, such as optical motion capture systems and wearable sen-
sors, with machine learning methods to assess the bradykinesia in PD [28–30] . These studies derived the classic
motion features and primarily achieved the distinction between healthy controls and PD patients. Compared
with these studies, this study employed the deep learning-based method to learn the latent features and subdi-
vide the bradykinesia in PD.

Clinical data often exhibit the characteristic of a small sample size. This paper employs a data augmentation
method using DTW data merge. It involves finding the optimal match between the two time series and then
concatenating them, ensuring that the concatenation points of the two time series have similar temporal char-
acteristics. Besides, DTW data merge can enhance robustness against noise and temporal misalignment in
time series [31] . The prototype learning method learns low-dimensional feature representations for time series