Power assistive devices have been developed in recent years. To detect the wearer’s motion, conventional devices require users to wear sensors. However, wearing many sensors increases the wearing time, and usability of the device will become worse. We developed a soft gait assistive suit actuated by pneumatic artificial rubber muscles (PARMs) and proposed its control method. The proposed suit is easy to wear because the attachment unit does not have any electrical sensors that need to be attached to the trainee’s body. A target application is forward walking exercise on a treadmill. The control unit detects the pre-swing phase in the gait cycle using the pressure information in the calf back PARMs. After the detection, the suit assists the trainee’s leg motion. The assist force is generated by the controlled PARM pressure, and the pressure input time is changed appropriately considering the gait cycle time. We conducted walking experiments; (1) verifies the proposed control method works correctly, and (2) verifies whether the gait assistive suit is effective for decreasing muscular activity. Finally, we confirmed that the accurate phase detection can be achieved by using the proposed control method, and the suit can reduce muscular activity of the trainee’s leg.
Various applications using pneumatic artificial muscles (PAMs) have been developed in recent years. When the pressure of the PAM is controlled, it is desirable to position a pressure sensor at the control port of a servo valve through a pipeline for improvement in usability, environment resistance, and elimination of mechanical complexity. However, the responsiveness of the pressure control worsens due to the volume and pressure losses through the pipeline. In this paper, we propose a pneumatic circuit model to compensate for such losses through the pipeline and a method to estimate the pressure at the PAM inlet. The proposed method improves the responsiveness of the pressure control by positioning a pressure sensor at the control port of the servo valve. We developed an experimental apparatus that simulates the mechanism of our power assist robot, and it was used for performance evaluation experiments of the conventional and proposed pressure control methods. When a sinusoidal pressure target value with a frequency of 1.5 Hz and load mass of 8 kg was fed as input, the errors between the measured and target values in the conventional and proposed control were approximately 45 and 20 kPa, respectively. The reduced error confirmed that the responsiveness of the pressure control was improved by the proposed method.
Toward reducing the effect of delay on motion transmission to a remote place, methods of forecasting human motion with subsecond preceding time have been studied. In this paper, we verified whether the prediction of single joint motion could be improved by using surface electromyography (EMG) signals. We used a recurrent neural network to predict the flexion and extension movement of a thigh, and compared the results between the prediction using only the angle and that using both the angle and EMG signals of two muscles. As a result, in the prediction of motion of about 0.5 Hz, the accuracy and delay of the prediction tended to be improved by using the EMG signals (e.g., in 0.3 s ahead prediction, the mean of the rootmean-square error between participants and trials is improved by 0.7°, and that of the prediction delay is reduced by 0.045 s). Such motion forecasting using EMG signals may be useful for improving the operability and stability of medical robots in telerehabilitation and telesurgery.
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