Prediction method of human defecation based on informer audio data augmentation and improved residual network.

Defecation care for disabled patients is a major challenge in health management. Traditional post-defecation treatment will bring physical injury and negative emotions to patients, while existing pre-defecation forecasting care methods are physically intrusive. On the basis of exploring the mechanism of defecation intention generation, and based on the characteristic analysis and clinical application of bowel sounds, it is found that the generation of desire to defecate and bowel sounds are correlated to a certain extent.

A study on robot force control based on the GMM/GMR algorithm fusing different compensation strategies.

To address traditional impedance control methods' difficulty with obtaining stable forces during robot-skin contact, a force control based on the Gaussian mixture model/Gaussian mixture regression (GMM/GMR) algorithm fusing different compensation strategies is proposed. The contact relationship between a robot end effector and human skin is established through an impedance control model. To allow the robot to adapt to flexible skin environments, reinforcement learning algorithms and a strategy based on the skin mechanics model compensate for the impedance control strategy.

A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features.

(1) Background: Incontinence and its complications pose great difficulties in the care of the disabled. Currently, invasive incontinence monitoring methods are too invasive, expensive, and bulky to be widely used. Compared with previous methods, bowel sound monitoring is the most commonly used non-invasive monitoring method for intestinal diseases and may even provide clinical support for doctors.

Research on a Defecation Pre-Warning Algorithm for the Disabled Elderly Based on a Semi-Supervised Generative Adversarial Network.

The elderly population in China is continuously increasing, and the disabled account for a large proportion of the elderly population. An effective solution is urgently needed for incontinence among disabled elderly people. Compared with disposable adult diapers, artificial sphincter implantation and medication for incontinence, the defecation pre-warning method is more flexible and convenient.

Robust seam tracking via a deep learning framework combining tracking and detection.

To address the problem of low welding precision caused by possible disturbances, e.g., strong arc lights, welding splashes, and thermally induced deformations, in complex unstructured welding environments, a method based on a deep learning framework that combines visual tracking and object detection is proposed.

An Adaptive Sliding-Mode Iterative Constant-force Control Method for Robotic Belt Grinding Based on a One-Dimensional Force Sensor.

To improve the processing quality and efficiency of robotic belt grinding, an adaptive sliding-mode iterative constant-force control method for a 6-DOF robotic belt grinding platform is proposed based on a one-dimension force sensor. In the investigation, first, the relationship between the normal and the tangential forces of the grinding contact force is revealed, and a simplified grinding force mapping relationship is presented for the application to one-dimension force sensors. Next, the relationship between the deformation and the grinding depth during the grinding is discussed, and a deformation-based dynamic model describing robotic belt grinding is established.

Learning Siamese networks for laser vision seam tracking.

To design a stable laser vision seam-tracking system, an advanced weld image processing algorithm based on Siamese networks is investigated and proposed to resist the interference of arc and spatter in the welding process. This specially designed neural network, combined with powerful feature expression capabilities of deep learning, takes two welding images with different sizes as inputs and generates a target confidence map in a single forward pass by using the cross-correlation algorithm. To prevent the error accumulation and model drift, an online update strategy via local cosine similarity is developed.