The pulse is a key biomedical signal containing various human physiological and pathological information highly related to cardiovascular diseases. Pulse signals are often collected from the radial artery based on Traditional Chinese Medicine, or by using flexible pressure sensors. However, the wrist wrapped with a flexible pressure sensor exhibits unstable signals under hand motion because of the concave surface of the wrist. By contrast, fingertips have a convex surface and therefore show great promises in stable and long‐term pulse monitoring. Despite the promising potential, the fingertip pulse signal is weak, calling for highly sensitive detecting devices. Here, a highly sensitive and flexible iontronic pressure sensor with a linear sensitivity of 13.5 kPa−1, a swift response, and remarkable stability over 5000 loading/unloading cycles is developed. This sensor enables stable and high‐resolution detection of pulse waveform under both static condition and finger motion. Fingertip pulse waveforms from subjects of different genders, age, and health conditions are collected and analyzed, suggesting that fingertip pulse information is highly similar to that of the radial artery. This work justifies that fingertip is an ideal platform for pulse signals monitoring, which would be a competitive alternative to existing complex health monitoring systems.

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A new method based on back propagation (BP) neural network for extracting RLCG parameter matrix of multi-core twisted cable is presented. With the properly selected parameter matrix sample, the variation characteristics of the parameter matrix of the multi-core twisted cable can be learned by the Levenberg-Marquardt (L-M) algorithm based on BP neural network. The proposed method is combined with the finite-difference time-domain (FDTD) method to calculate the near end crosstalk (NEXT) and the far end crosstalk (FEXT) of the multi-core twisted cable. To verify the new method, a three-core twisted cable is measured and analyzed in the frequency band of 100 kHz-1 GHz. The results show that the verification error of the extraction network of the RLCG parameter matrix has good accuracy, which does not exceed 0.8%. Compared with the full wave method, the maximum deviations of FEXT and NEXT solved by the proposed methods are −2.71 dB and 10.56 dB, respectively, which are better than 29.32 dB and 32.45 dB solved by the conventional method. INDEX TERMS Multi-core twisted cable, crosstalk, finite-difference time-domain (FDTD) method, neural network.