Traditional Chinese medicine(TCM) has existed for more than two thousand years and one of the important diagnostic methods is the pulse diagnosis. It generally takes decades of training for a practitioner to master this skill as pulse acquisition and diagnosis require long-term experiences and are very subjective. The project aims to use the combination of advanced sensor technology and artificial intelligence to emulate the TCM practice for health monitoring. A flexible piezoelectric film is designed to record the wrist pulse data. A group of representative pulse features has been extracted from the volunteer data and fitted into a machine learning algorithm. The trained machine learning model has been proved to be effective in telling different health conditions of volunteers. For future directions, we are planning to apply machine learning algorithms for the inverse design of sensor devices, and guide the fabrication process of sensor design. The potential has been proved by other MEMS device designs.
Project ended 08/01/2022