Digital Human Research Group
Human Informatics Research Institute (HIRI)
National Institute of Advanced Industrial Science and Technology (AIST)
|2006/09 - 2009/09 :||The University of Tokyo, Graduate School of Information Science and Technology|
|2009/10 -2011/10 :||Disney Research, Pittsburgh, Postdoctoral Researcher|
|2011/11 - 2012/02 :||Carnegie Mellon University, RI, Postdoctoral Researcher|
|2012/03 - 2013/03 :||The University of Tokyo, Graduate School of Information Science and Technology, Project Researcher|
|2013/04 - 2015/03 :||The University of Tokyo, Graduate School of Information Science and Technology, Project Assistant Professor|
|2015/04 - :||National Institute of Advanced Industrial Science and Technology (AIST), Science Researcher|
- 2004: Japan Society of Mechanical Engineers, Miura Prize
- 2009: Finalist of 14th Robotics Symposia Prize
- 2009: Society of Instrument and Control Engineers, SI Division, Research Encouragement Award
- 2010: Japan Society of Mechanical Engineers, ROBOMEC Prize
- 2011: Disney Inventor Award
- 2016: Robotics Society of Japan, Research Encouragement Award
- 2016: Super Human Sports Academy Conference, Encouragement Award
Whole-body Detailed Musculoskeletal Model and Volumetric Skin-musculoskeletal Model and Their Kinematics and Dynamics Analysis
We develop the whole-body musculoskeletal model that represents the kinetic and anatomical characteristics of human body. The skeleton model is created by getting the CT scan of the adult-male skeleton specimen. The kinematics and dynamics computation is enabled by implementing the skeleton structure and inertial parameters of the model. We implement the musculo-tendon network with attaching the muscle insertion and via points on the skeleton model based on the anatomical knowledge. This model consists of 989 muscles, 50 tendons, 117 ligaments, and 34 cartilages, and becomes one of the most detailed model in the world.
We realize the kinematics and dynamics computation of this whole-body musculoskeletal model. The somatosensory information is estimated from the human motion data based on the kinematics and dynamics computation framework that is developed in the robotics field. We consider the constraints that come from the human nerve system (e.g. somatosensory reflex system) in the process of this estimation. Our algorithm realizes the human-like somatosensory information estimation, e.g. estimates the similar muscle activities in the synergistic muscles.
We elaborate this musculoskeletal model and develop the volumetric skin-musculoskeletal model that represents the anatomical shape of the surface skin, bones, and muscles. The skeletal subspace deformation (SSD), which is the skinning technique for the character animation, is extended and applied to estimate the skin and muscle deformation with considering their interaction in low computational cost. Our model estimates the muscle moment arm, which is the critical parameter to estimate the somatosensory information, much more precisely than the previous model. This technique realizes the estimation of the human-like somatosensory information in low computational cost.
Real-time Muscle Tension Estimation and Its Visualization
We realize the real-time estimation of the somatosensory information using the optical motion capture system, force plates, and EMG, and visualize this information with the musculoskeletal model. It is known in the sports field that providing the somatosensory information in real-time improves the athlete's performance. Quantifying the motion and somatosensory information in real-time enables the intervention and modification of the human motion, and would become the framework of analyzing the human motion generation / control mechanism.
Neuromuscular Locomotion Controller that Generalizes Human-like Response to Tripping
We develop the neuromuscular system based on the anatomical nerve structure, and identify the system parameters using the experimental human motion data. The identified system is applied as the controller for the forward dynamics simulation of the human motion. This controller realizes the human-like locomotion and responses to tripping. This result shows the possibility that the human fall-avoidance response to tripping is generated by the somatosensory reflex system, and the training that facilitate the appropriate reflex would realize the robust locomotion to tripping.
- Murai A., Hobara H., Hoashizume S., Kobayashi Y., Tada M., "Modeling and Analysis of Individual with Lower Extremity Amputation Locomotion Using Prosthetic Feet and Running-specific Prostheses", IEEE EMBC 2017, 2016.
- Murai A., Hong Q., Hodgins J., Yamane, K., "Dynamic Skin Deformation Simulation Using Musculoskeletal Model and Soft Tissue Dynamics", Computational Visual Media, pp.1-12, 2016.
- Murai A., Endo Y., Tada M., "Anatomographic Volumetric Skin-Musculoskeletal Model and Its Kinematic Deformation With Surface-Based SSD", IEEE Robotics and Automation Letter, pp.1103-1109, 2016.
- Murai A., Yamane K.,"A Neuromuscular Locomotion Controller that Realizes Human-like Responses to Unexpected Disturbances", 2011 IEEE ICRA, pp.1997-2002, 2011. (Disney Inventor Award)
- Murai A., Kurosaki K., Yamane K., Nakamura Y., "Musculoskeletal-see-through Mirror: Computational Modeling and Algorithm for Whole-body Muscle Activity Visualization in Real Time", Progress in Biophysics and Molecular Biology, 103(2), pp.310-317, 2010. Etc.