Micro Macro Neural Network to Recognize Slow Movement: EMG based Accurate and Quick Rollover Recognition

Non

Motion Control for an Intelligent Walking Support Machine

Walking is a vital exercise for health promotion and fundamental ability necessary for everyday life. Up to now, many robots for walking support or walking rehabilitation of the elderly and the disabled are reported. In this paper, a new omni-directional walking support machine is developed. The machine can realize walking support by following the user's control intention which is detected according to the user's manipulation. However, the motion of the machine is affected by the nonlinear frictions, center-of-gravity (COG) shifts and loads changes caused by users. It is necessary to improve the machine's motion performance to follow the user intention and support the user. Therefore, this paper describes a motion control method based on digital acceleration control to deal with the problem of nonlinear frictions, COG shifts and loads changes. Simulations are executed and the results demonstrate the feasibility and effectiveness of the proposed digital acceleration control method

Improving the Motion Performance for an Intelligent Walking Support Machine by RLS Algorithm

To make the old people and handicapped people move easily by themselves, an omni-directional walking support machine (WSM) has been developed. In our previous study, to improve the motion performance of the WSM, a digital acceleration control method has been developed to deal with the nonlinear friction. However, the design of the digital acceleration controller requires to know the exact plant parameters of the WSM which are variable due to center of gravity (COG) shift and load changes. The change of the plant parameters affects the motion performance of the digital acceleration control system. Therefore, in this paper, a discrete-time system identification method using recursive least squares (RLS) algorithm is proposed to online identify the WSM’s plant parameters for the digital acceleration controller. Simulations are executed and compared with the digital acceleration controller without using RLS algorithm, and the results demonstrate the feasibility and effectiveness of the proposed control method

Preliminary in vivo evaluation of a needle insertion manipulator for central venous catheterization

Central venous catheterization is associated with potential complications secondary to accidental puncture, including venous bleeding and pneumothorax. We developed a system that avoids these complications and simplifies the procedure using a robot to provide puncture assistance. We herein report a puncture experiment conducted in vivo in a porcine to evaluate the manipulator. The right and left jugular veins of a pig were punctured five times each through both opened and unopened skin at a puncture angle and speed. A venous placement rate of 80% was obtained with opened skin. A much lower rate of 40% was obtained with unopened skin. One of five attempts in opened skin was unsuccessful, likely because of the stick-slip phenomenon. This system was effective for jugular venous puncture of opened skin. Future studies should focus on puncture conditions that facilitate needle placement, inhibit the stick-slip phenomenon, and minimize needle bending due to the presence of skin. © 2014 Kobayashi et al.; licensee Springer.1

Prediction Algorithm of Parameters of Toe Clearance in the Swing Phase

The adaptive control of gait training robots is aimed at improving the gait performance by assisting motion. In conventional robotics, it has not been possible to adjust the robotic parameters by predicting the toe motion, which is considered a tripping risk indicator. The prediction of toe clearance during walking can decrease the risk of tripping. In this paper, we propose a novel method of predicting toe clearance that uses a radial basis function network. The input data were the angles, angular velocities, and angular accelerations of the hip, knee, and ankle joints in the sagittal plane at the beginning of the swing phase. In the experiments, seven subjects walked on a treadmill for 360 s. The radial basis function network was trained with gait data ranging from 20 to 200 data points and tested with 100 data points. The root mean square error between the true and predicted values was 3.28 mm for the maximum toe clearance in the earlier swing phase and 2.30 mm for the minimum toe clearance in the later swing phase. Moreover, using gait data of other five subjects, the root mean square error between the true and predicted values was 4.04 mm for the maximum toe clearance and 2.88 mm for the minimum toe clearance when the walking velocity changed. This provided higher prediction accuracy compared with existing methods. The proposed algorithm used the information of joint movements at the start of the swing phase and could predict both the future maximum and minimum toe clearances within the same swing phase