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Gait information of pedestrians is an important factor in gait rehabilitation. The most important factor is to measure the center of pressure (CoP) compared to other measurements because gait patterns are varied according to CoP. Multiple strain gauges, multi-axis load cells, and pneumatic tube sensors have been used to measure the CoP of the body. Because these sensors are large sizes and high prices, these sensors are not suitable for low cost gait rehabilitation robots. Thus, in this study a sensor module that can estimate the CoP of the body roughly is proposed. The proposed sensor module uses resistance variable sensors, which are able to detect positions. For applying the proposed sensor, an extra device is proposed and is combined with sensors for configuring a module. A comparison test is implemented using a pressure sensor with an FSR array structure in order to verify the proposed method. In results, the proposed method represents an agreement in directivity more than 93%.

The falling off of body functions occurs gait disturbances as the human body is aged. Rehabilitation training is needed for overcoming such gait disturbances and the training can be achieved through repetitive training. Recent robot-assisted rehabilitation training has been usually performed in indoors and training speed is to be passively selected. Thus, in this study a training method that implements mobile gait rehabilitation training and varies training speed intellectually is proposed. The proposed method estimates gait intentions and information in training subjects and that is used in determining training speed.

In this paper, we propose the algorithm that improves the linearity of the walking assistant robot on lateral slopes. The walking assistant robot goes out of the course due to the rotational moment which is caused by the weight of the robot and the slope. To compensate this, we give the weight to each driving axle after comparing the real rotational angular velocity with the target rotational angular velocity which is entered by an user. The results of applying the algorithm to the real walking assistant robot show that the yaw axis deviation of the robot without the algorithm diverges, but the yaw axis deviation of the robot with the algorithm lies within 20cm, which can be recognized as stable. In addition, the changing rate of the course deviation is stabilized and shows no more course deviation, after moving 300cm.