Prof. Warisawa’s current research focuses on wearable/ambient human health monitoring. He has developed many sensing devices and systems for human behavior recognition, human health monitoring and ambience communication. In the systems, he has employed information technology based on statistical analysis and machine learning.
“Sensing system design and evaluation towards supersensing”
Monitoring health, stress and emotion condition and how we can get better condition based on such information are strongly required in the era of Society 5.0. In this talk, a wearable blood pressure monitoring, food habit monitoring, and ambient communication system are presented in order to give an easier understanding of speaker’s research area, covering from a device development to information processing such as machine learning. Especially, a cuff-less, calibration-free and continuous blood pressure monitoring system is focused. The cuff-less style based on a pulse wave velocity (PWV) method with photoplethysmogram and electrocardiogram (ECG) sensors attached to the body gives better comfort to users. No calibration in the PWV method is now required by machine learning techniques with features extracted from ECG and PPG signals. Continuous blood pressure monitoring verification tests gives capability to catch important symptoms such as short-term changes and changes during the day.