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In the actual emergency cardiac compression process, it is necessary to continuously press multiple times in a short period of time. It is inevitable that there are problems such as obstruction and jitter during distance measurement. Moreover, in most cases, it is difficult to achieve high-precision millimeter-level measurement, therefore, it is difficult to obtain a large number of reliability data labels. In order to solve the problem of accurate training set labels, we use high-speed cameras and ultrasonic rangefinder to correct them. With high speed camera recordings, readings can be taken directly on the range scale. Ultrasonic ranging can reach an accuracy 1mm when measuring short distances. An ultrasonic rangefinder is installed inside the emergency care simulator to measure the compression distance. Because of that, ultrasonic rangefinder is only used as a correcting tool.\\
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After the data preprocessing, as another feature of the sampling, the input data set for CNN has missing values due to the inconsistencies at the endpoints, which can be seen from Figure \ref{fig:dataalign}. We adapt the model for different wave lengths and sampling rates in two ways: (1) change the neutral node sizes to accommodate the number of the wave inputs; (2) padding or segmenting each piece to make it fit for the input dimensions of the network.The first one implies modifying the size of the first convolutional layers as well as the number and the dimension of model. Then, we consider the inputs alignment. However, after we align the inputs, the data of the starting point alignment is not easy to observe the discrete condition of the wave of compression signals, then all the waves are converted from the origin alignment to the maximum alignment.In order to let the input data have the same size and prevent the occurrence of missing values, each curve takes the maximum point as the boundary and takes 50 points on the left and 20 points on the right to form a data set. These waves are drawn in the same picture for convenience display, as shown in Figure ~\ref{fig:dataalign}.Now, The length of waves have the same points. Otherwise, zero value is filled.

\begin{figure}
\centering
\includegraphics[width=0.8\textwidth]{fig_dataalign.eps}
\caption{Data alignment of normalized waveform data. The waveform of the left figure is aligned by the start points and the right is by the maximums point }
\label{fig:dataalign}
\end{figure}
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A preliminary analysis of the data curve containing the label and the maximum alignment graph shows that the wave similarity is high when they are denoting valid CCP. When the gap between the compression distance exceeds 5mm, there is a large jitter in the middle of the compression signals. If the interval of two compressions is too long, the waveform display are abnormal compared with the standard one. There are also obvious correct waves, which are concentrated on the two extreme points of the wave set that are too smooth or too prominent. Therefore, the variance at the two extreme points can be used to measure the degree of discreteness of the data, which may be another important character.
