Elucidation of biological functions based on cybernetics and systems theory is essential to development of new therapeutic devices and biomedical control methods. By use of information technology, computer sciences, system engineering and control engineering, the Division of Cybermedicine and Biocybernetics studies modeling, state estimation and simulation techniques for biological systems, and also aims at development of not only optimal control technologies for artificial organs, rehabilitation devices and healthcare systems but also high-performance human interface systems.
Tele-healthcare system based on correlation analysis of multi-dimensional biosignals obtained from distributed sensors
Tele-medical system using the Electronic Doctor's Bag
By using multi-modal imaging techniques, we aim to accurately visualize wide range of biomedical phenomena such as microscopic neuronal activities as well as macroscopic whole brain functions. The images can then be utilized for not only extracting useful biomedical information, but also revealing mechanisms of neural information processing. We also build computational models of neural information processing inspired by highly intellectual cognition skills of human specialists, such as medical image diagnosis by radiologists. Based on the models and some machine learning techniques, we develop intelligent computer-aided systems for medical image diagnosis, interventional radiology, and image-guided therapy.
Multi-cellular imaging (left) and whole brain imaging (middle) for revealing brain functions and novel intelligent imaging techniques (right: tumor extraction from x-ray fluoroscopy) for diagnosis and therapy.
Recently, the current era is referred as a century of robotics and AI. However, robot capability in real life is still rather limited then there are still a lot of things we need to deeply learn from advanced and robust motor control and sensory functions which humans have, for next step forward. Robotics is also useful as computational tool to understand human motor learning mechanism. Neuroscience knowledge can be useful to improve robot capability. We study on neuroscience for robotics and robotics for neuroscience as [Neuro-Robotics].
NIRS-EEG joint imaging during transcranial direct current stimulation
Muscle volumetric modeling for function, physiology and deformation
Balance estimation independent from foot pressure measurement