Development of microcatheter tube extrusion angle estimation system using convolutional neural network segmentation.

This study presents a deep learning-based monitoring system for estimating extrusion angles in the manufacturing process of microcatheter tubes. Given the critical nature of these tubes, which are directly inserted into the human body, strict quality control is imperative. To mitigate potential quality variations stemming from operator actions, a system utilizing a convolutional neural network to precisely measure the extrusion angle-a parameter with profound implications for tube quality-is developed. Until now, there has been no method to estimate the extrusion angle of resin being extruded in real-time. In this study, for the first time, a method using deep learning to estimate the angle was proposed. This innovative system comprises two RGB cameras capturing both front and side perspectives. The acquired images undergo segmentation via a meticulously trained convolutional neural network. Subsequently, the extrusion angle is accurately estimated through the application of principal component analysis on the segmented image. The usefulness of the proposed system was rigorously confirmed through comprehensive validation measures, including mean intersection over union (mIoU), mean absolute angle error (MAE), and inference time, using a real-world dataset. The attained metrics, with an mIoU of 0.8848, MAE of 0.5968, and an inference time of 0.0546, unequivocally affirm the system's suitability for enhancing the catheter tube extrusion process.

Approximation Model Development and Dynamic Characteristic Analysis Based on Spindle Position of Machining Center.

To evaluate the dynamic characteristics at all positions of the main spindle of a machine tool, an experimental point was selected using a full factorial design, and a vibration test was conducted. Based on the measurement position, the resonant frequency was distributed from approximately 236 to 242 Hz. The approximation model was evaluated based on its resonant frequencies and dynamic stiffness using regression and interpolation methods. The accuracy of the resonant frequency demonstrated by the kriging method was approximately 89%, whereas the highest accuracy of the dynamic stiffness demonstrated by the polynomial regression method was 81%. To further verify the approximation model, its dynamic characteristics were measured and verified at additional experimental points. The maximum errors yielded by the model, in terms of the resonant frequency and dynamic stiffness, were 1.6% and 7.1%, respectively.

Three-Dimensional Foot Position Estimation Based on Footprint Shadow Image Processing and Deep Learning for Smart Trampoline Fitness System.

In the wake of COVID-19, the digital fitness market combining health equipment and ICT technologies is experiencing unexpected high growth. A smart trampoline fitness system is a new representative home exercise equipment for muscle strengthening and rehabilitation exercises. Recognizing the motions of the user and evaluating user activity is critical for implementing its self-guided exercising system. This study aimed to estimate the three-dimensional positions of the user's foot using deep learning-based image processing algorithms for footprint shadow images acquired from the system. The proposed system comprises a jumping fitness trampoline; an upward-looking camera with a wide-angle and fish-eye lens; and an embedded board to process deep learning algorithms. Compared with our previous approach, which suffered from a geometric calibration process, a camera calibration method for highly distorted images, and algorithmic sensitivity to environmental changes such as illumination conditions, the proposed deep learning algorithm utilizes end-to-end learning without calibration. The network is configured with a modified Fast-RCNN based on ResNet-50, where the region proposal network is modified to process location regression different from box regression. To verify the effectiveness and accuracy of the proposed algorithm, a series of experiments are performed using a prototype system with a robotic manipulator to handle a foot mockup. The three root mean square errors corresponding to X, Y, and Z directions were revealed to be 8.32, 15.14, and 4.05 mm, respectively. Thus, the system can be utilized for motion recognition and performance evaluation of jumping exercises.

Effect of Auditory Feedback on Tactile Intensity Perception in a Touchscreen Application.

This article presents the effect of auditory feedback on tactile intensity perception, which may be of interest to haptic or audiotactile interaction engineers. An experimental setup consisted of a touchscreen, an electrodynamic shaker, and a closed-back headphone for a subject to interact with the touchscreen and to feel audiotactile feedback. In the experiment, participants were asked to judge perceived tactile intensity, using the magnitude estimation method, in the absence and presence of simultaneous auditory feedback. All data collected from the subjects were analyzed statistically, and then the effect of auditory feedback was investigated focusing on the following aspects: whether the presence of auditory feedback changes perceived tactile intensity, whether the frequency component of auditory feedback affects tactile intensity perception, and whether the coincidence of tactile and auditory frequencies influences on tactile intensity perception. Besides, changes in Stevens's exponent were analyzed to discuss how tactile intensity perception varies due to the auditory feedback. Finally, an equal intensity contour, in the domain of sensation level and frequency of tactile stimulation, was drawn. It can be applied to adjust the level of tactile stimuli for haptic feedback designers to provide a constant perceived tactile intensity considering the presence of auditory feedback.