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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 78

Figure 2. Marker location of motion capture system in finger tapping (top) and toe tapping tasks (bottom).

Figure 3. Example of the optimal warping path.

were augmented two times using the DTW data merge method for the network training, and the final sample
size of the training set is 360 for both the finger and toe tapping tasks.

2.2 DTW-based data augmentation
This paper adopts the DTW data merge method to achieve data augmentation, which can address the problem
of temporal information distortion by preserving temporal relationships during augmentation. It employs a
DTW algorithm to obtain the optimal match between the two signals. This algorithm stretches or compresses
the two signals to identify corresponding similar points. The set of these corresponding points is referred to
as the optimal warping path [Figure 3].

For two signals, = ( 1 , 2 , ..., ) with elements and = ( 1 , 2 , ..., ) with elements, the optimal
warping path is obtained using the DTW algorithm as = ( 1 , 2 , ..., , ..., ), where = ( , , , ),
= 1, 2, ..., , = 1, 2, ..., , and = 1, 2, ..., . , and , denote the corresponding points of the two

signals. Afterobtainingtheoptimalwarpingpathbetweenthetwosignals, arandomelement = ( , , , )

wasselectedwhere ischosenfromaGaussiandistribution N ( , ). Accordingto , the and aresliced
2 10
, ..., ).
and concatenated to generate a new time series = ( 1 , 2 , ..., , , ,