Title Multi-channel Bio-signal-based Human Movement Estimation for Assistive Robot Control

Robot technologies have been widely introduced in industrial fields so far, and improved work efficiency. The robot research in the industrial have mainly focused on the autonomous of robot under the environment where humans do not intervene. However, thanks to recent highly-developed control and sensor technologies, the robot has become possible to assist human movement and work with human. For example, exoskeleton robot to assist human movement and general-purpose type humanoid robot are actively investigated. A major feature of these robots is to physically interact with human. In particular, since the assistive robot is controlled by the user wearing it, the human state changing dynamically has to be considered. Bio-signals have been extensively used to control assistive robot by estimating human movement intentions. For these applications, using multiple sensor channels is effective means to estimate user’s movement intentions in detail. However, multi-channel bio-signal-based control has yet to be feasible technology in real world applications due to the difficulty of signal processing for the control. A robust estimation of human motor intention will be a promising approach for the high affinity robot control. In this study, I propose robust estimation methods of human motor intention from multi-channel Electromyography (EMG) or Electroencephalogram (EEG) to control assistive devices, and develop hardware and software to realize the systems. In the EMG-based assistive robot control, estimating human joint torques robustly from muscle activities is the basic challenge. Most of conventional studies estimated joint torques from small number of electrodes based on a model calibrated by a data set corresponding to static load, and controlled the assistive robots with small degree of freedom. However, since the muscle activities are different between the static and dynamic motion, conventional data acquisition method can not describe the relations between joint torques and EMG during motion. Second, since assistive exoskeleton robots can make physical contact with environments or the limbs of human user, sensor electrodes and human motor intention might be affected. For example, the sensor electrodes can be dislodged from the users or damaged by collisions in the physical interaction. In addition, the probability of sensor electrode misplacement by human error increases as well as the sensor fault occurrence with multiple sensor electrodes. Such sensor anomalies as the disconnection, detaching of electrodes, and electrode misplacement cause significant errors in the estimation of user movement and cause large unwanted interaction between the user and robot. It seems difficult to use many sensor channels to control assistive robots, although using multiple sensor electrodes is useful to estimate user movement intentions. In addition, if the exoskeleton robot assist human joint movement as the consequence of interaction, the human intentional joint torques are affected, and the original estimation cannot be used. In this study, I propose methods to deal with above problems. For the model calibration, torque-EMG data set during dynamic motion are acquired by introducing inverse dynamics and bioengineering knowledge. Based on the data set acquisition, I propose robust estimation model using redundancy of the multi-channel bio-signals to control assistive robot with multi-degree of freedom by applying machine learning technologies. In addition, I