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This article simulates the actual situation of CC during the data collection process. Each set of data is the acceleration curve collected during continuous compression. However, the data collected by time point is affected by electromagnetic and voltage, and the original data contains a lot of white noise. And each group of experiments includes several compressions that create several waveform pulse signals. It is necessary to find the pulse generated by the pressing from the original waveform, remove the interference signals, and distinguish the obtained waveform to obtain the corresponding acceleration changes that are corresponding to each compression. Therefore, data preprocessing such as noise reduction filtering, pulse recognition, waveform segmentation and fitting is required, as shown in Figure ~\ref{fig:datapre}.
\subsubsection{Denoising filtering}
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As shown in \cite{Bao2019}, this is accordance with other mentioned researches. According to the influence of electromagnetic and voltage at the sampling time, the data directly collected by the experimental equipment contains a lot of white noise, and there are many burrs in the original pressing waveform. If it is not reduced, it will seriously interfere with the analysis and processing of the compression data. The filtering method has been tested. Certainly,  the effect of single-dimensional Kalman filter is better. However, its operation time and complexity are high, which makes the operation delay on a microcontroller. In the case of small losses, the median filtering and low-pass amplitude-controlled filtering are adapted in this paper.
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By analyzing the raw data, the low-pass amplitude-controlled filtering is first used to filter the unsuitable white noise and hammering anomalies before performing waveform analysis. After passing through the low-pass filter, there is a significant correlation between adjacent sampling points, and the narrower the filtering bandwidth, the stronger the correlation.Then, using median filtering through sliding window. This has the advantage of suppressing periodic interference and has high smoothness.
\subsubsection{CC pulse recognition}
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In this case, the gradient and amplitude feature calculation method are used to identify the pulse, and then the sliding window is used to distinguish the waveform of each pressing pulse. Then, waveform verification is necessary. The width of the waveform limits the time consumption of CC and the height limits the strength of CC. The three gradient values of the waveform will obtain a normalized sensing data of CC.
The specific process is as follows:\\
Step1: Establish two sliding windows. One named sliding window A stores the sampled value, and the other called sliding window B is to store the filtered result;\\
Step2: Do threshold monitoring on the sliding window B, and determine the threshold value when the static gravity value shows an $\alpha$ percent change;\\
Step 3: Cut the wave in the window B. To select the optimal point from window B or A as the starting point and terminate on the pulse end point, which physical meaning is a compression process. In the cutting wave , the hand speed is zero when the compression reaches the lowest point position; \\
Step4: Recognize the CC pulse according to three restrictions.\\
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Then a set of standardized waveforms will be obtained. According to this method, pulse identification and waveform segmentation were sequentially performed on 18 sets of experimental data, and finally 975 CC waveforms were obtained.
