First Name
Zhengyong
Last Name
Liu
Dataset Entries from this Author
The dataset contains performance data of test sensors and pulse test data. The pressure sensitivity of the CCFPI sensor is ~0.612 nm/N. Based on the special nonlinear property of the circular structure, accurate pulse waveforms can be acquired for blood pressure prediction. By constructing a deep learning model, the blood pressure could be directly retrieved based on the pulse waveforms measured by CFPI pressure sensor. As a result, the measurement errors of SBP and DBP were -0.122 ± 2.781 mmHg and 0.051 ± 1.711 mmHg, respectively.
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1.The spectrum of the dataset is obtained by applying force to the tactile sensor based on Chirped Bragg gratings.
2.The applied force ranges from 0N to 10N on the sensing pad of 4cm×4cm.
3.The folder name (x, y) represents the specific coordinates of the point at which the force is applied, and the xN name of the subfolder represents the xN force applied at that point.
4.A total of 120 spectral data were collected in each applied force state.
5.The first column of each spectrum is wavelength and the second column is intensity.
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