Research Keyword: Ultrasound, wearable, machine learning, automatic evaluation

In this research cluster, our goal is to sense biomedical objects and areas that have been impossible to measure by traditional methods. To realize it, we apply our specialties, flexible ultrasonic sensors, high-performance electric circuits, visualization technology, and machine learning.

The sol-gel composite ultrasonic sensors developed by Dr. Tanabe and Prof. Kobayashi are flexible and extremely thinner than conventional ultrasonic sensors, and it could be an effective patch type sensor. Dr. Yamakawa is developing electronic circuit devices that enables it to transfer echo signals to an analysis computer wirelessly.

  In our team, we are developing patch-type wireless ultrasonic sensors. In our goal, the measurement results will be automatically classified into three cardiac abnormalities: coronary artery disease (CAD), myocardial infarction (MI), and congestive heart failure (CHF), by machine learning by Dr. Acharya and Mr. Oh, Ngee Ann Polytechnic, Singapore, with advice from MD Tan, National Heart Centre Singapore, Singapore.