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Shu et al. Intell Robot 2024;4(1):74-86 I http://dx.doi.org/10.20517/ir.2024.05 Page 84

to reduce the data requirements [23] . The attention mechanism, calculating the distance between prototypes
and feature embeddings for classification, can also address the issue of small sample sizes [32] . Comparative
results with traditional classification methods demonstrate the effectiveness of the proposed method. Further-
more, compared to the CPN classification method, the result indicates that the attentional prototype networks
significantly improve classification performance. The attentional prototype network, effective for addressing
small-sample classification problems, incorporates an attention mechanism that enhances the extraction of
delayed features. This improvement contributes to improved performance in the classification of time-series
signals [33] .

Currently, the assessment of bradykinesia in PD primarily relies on clinical scales. Despite the application of
various intelligent instruments to achieve more objective and accurate quantitative assessments, there remains
a lack of detailed differentiation of bradykinesia, and the classification accuracy can be further improved [10,34] .
This paper introduces an effective solution for the accurate assessment of the degree of bradykinesia in PD
by combining high-precision motion capture data and a small-sample classification method designed for time
series signals.

This study has some limitations, which are planned as the focus of our future research. One main limitation
is that PD is a heterogeneous condition rather than a disease. Hence, it is ambitious to draw generalizable
conclusions. Besides, the test-retest reliability is absent in our study. Larger sample sizes with segmented PD
types should be considered in future work to further validate our method’s effectiveness and the test-retest
reliability. On the other hand, the previous study has explored the potential of integrating multimodal signals,
whichimprovedthemotionfunctionassessment [35] . Theproposedmethodcanbeextendedtothemultimodal
signals, which promises to achieve a more accurate assessment of bradykinesia in PD.

5. CONCLUSIONS
This paper addresses the classification of small-sample time series signals and proposes a classification method
based on DTW data merge and attentional prototype networks. Firstly, the method employs DTW data merge
for data augmentation. Subsequently, a random grouping method is used to reorganize the dimension of time
series, followed by convolution operations to extract features in multivariate time series. The attention mech-
anism and prototype learning are introduced to train low-dimensional feature representations of time series,
thus reducing the dependency on data volume. The proposed method is applied to motion capture data of
upper and lower limb movements. Experimental results indicate that the DTW data merge method, atten-
tion mechanism, and prototype learning modules effectively reduce the data volume requirements. Addition-
ally, the use of attention prototype networks significantly improves classification performance. The proposed
method can be effectively applied to the classification of small-sample time series signals and achieve an accu-
rate assessment of the degree of bradykinesia in PD.

DECLARATIONS
Acknowledgments
The authors would like to thank the editor-in-chief, the associate editor, and the reviewers for their valuable
comments.

Authors’ contributions
Made substantial contributions to the research and investigation process, reviewed and summarized the liter-
ature, and wrote and edited the original draft: Shu Z, Liu J
Provided administrative, technical, and material support: Liu P, Cheng Y, Feng Y
Performed critical review, commentary, and revision: Zhu Z, Yu Y, Han J, Wu J, Yu N