Yuichiro Yoshikawa

Power assistance on slope of an omnidirectional hybrid walker and wheelchair

In this paper, first we briefly present an overview of our developed electric powered user-propelled hybrid walker and wheelchair robot. Then we focus on analysis and investigation of a movement in which the robot starts to ascend and to descend a slope. We originally point out that in such motion the users walking speed and the robots traveling speed are inevitably different since the robot is undergoing a planar motion. This explains why the load of the user suddenly increases/decreases when a user-propelled walker or wheelchair starts to ascend/descend a slope. Note that as far as our knowledge this is the first time clarifying the intrinsic nature of the movement. Further we propose two speed compensation approaches under an admittance based interaction control to let the user get a smooth “load feeling” as the user utilizing the robot on a horizontal plane. The experiments verify the effectiveness of the proposed approaches.

EMG estimation from EEGs for constructing a power assist system

Brain-Machine Interface (BMI), has been becoming gradually an effective way to help and support the disabled person by using his/her brain activity information. It is also expected to be used for both of the disabled and the healthy people. In this paper, aiming to estimate the force/torque information from brain activity to help and support humans daily life, we estimate the humans muscular activity from elec-troencephalography (EEG) by Principal Components Analysis (PCA) and Recursive Least Squares (RLS). The concept of the proposed approach using PCA and RLS is explained, and then the proposed approach is verified by experiments. The results show that the estimation of electromyography (EMG) from EEG is possible and this implies a great potential to use EEG signals for supporting human activities.

Power assistance of an omnidirectional hybrid walker and wheelchair with admittance model and Iterative Learning Control

In this paper, at first we briefly present an overview of our developed hybrid walker and wheelchair robot. Then we focus on analysis and investigation of the movement in which the robot starts to ascend/descend a slope, and point out that the users walking velocity and the robots velocity are inevitably different. Further, in order to let the user get a smooth load feeling as the user use the robot on a horizontal plane, we propose V.C (Velocity Compensation) and ILC (Iterative Learning Control) to adaptively adjust the control parameter. The experiments verify the effectiveness of the proposed approaches.

Motion support of upper extremity with agonist alone under negative admittance control

In this paper, different from the conventional admittance control, an originally negative admittance control, in which two virtual parameters, virtual damping coefficient and virtual moment of inertia, are negative, is developed to support the movement of humans upper extremity only by agonists EMG signals. First, we empirically determine two agonists, biceps brachii and clavicular part of deltoid to get their EMGs respectively as the control signals for elbow joint and shoulder joint. Then a negative admittance control is developed to support the movement of humans upper extremity for holding up, keeping and holding down a load only by agonists EMG signals. The experiments demonstrate that seamless movements of holding up, keeping and holding down a heavy load is successfully realized. The experimental results show that the performance of the negative admittance control can be arbitrarily adjusted by control parameters and users “load feeling” can be arbitrarily reduced.