Hsieh T.-CWu C.-MTsai C.-CLo W.-CWang Y.-MSmith S.SHANA SMITH2022-03-222022-03-22202114248220https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108403047&doi=10.3390%2fs21134339&partnerID=40&md5=08200970df660a9fb2ce78fc7447bac5https://scholars.lib.ntu.edu.tw/handle/123456789/598994Pulse palpation is an effective method for diagnosing arterial diseases. However, most pulse measurement devices use preconfigured pressures to collect pulse signals, and most pulse tactile simulators can only display standard or predefined pulse waveforms. Here, a portable interactive human pulse measurement and reproduction system was developed that allows users to take arbitrary pulses and experience realistic simulated pulse tactile feedback in real time by using their natural pulse-taking behaviors. The system includes a pulse tactile recorder and a pulse tactile player. Pulse palpation forces and vibrations can be recorded and realistically replayed for later tactile exploration and examination. To retain subtle but vital pulse information, empirical mode decomposition was used to decompose pulse waveforms into several intrinsic mode functions. Artificial neural networks were then trained based on intrinsic mode functions to determine the relationship between the driving signals of the pulse tactile player and the resulting vibration waveforms. Experimental results indicate that the average normalized root mean square error and the average R-squared values between the reproduced and original pulses were 0.0654 and 0.958 respectively, which indicate that the system can reproduce high-fidelity pulse tactile vibrations. ? 2021 by the authors. Licensee MDPI, Basel, Switzerland.Artificial neural networkEmpirical mode decompositionIntrinsic mode functionsPulse tactile playerPulse tactile recorderCell proliferationDiagnosisDisplay devicesFunctionsMean square errorNeural networksDisplay standardsEmpirical Mode DecompositionIntrinsic Mode functionsPulse measurementsRoot mean square errorsTactile explorationTactile feedbackVibration waveformsSignal processingheart ratehumanpalpationpressurepulse ratetouchHeart RateHumansNeural Networks, ComputerPalpationPressurePulseTouch[SDGs]SDG3journal article10.3390/s21134339342019542-s2.0-85108403047