Diazwilkinson4778

In addition, when combined with other adaptive RF techniques, the GRCNN demonstrated competitive performance to the state-of-the-art models on benchmark datasets for these tasks.We consider the problem of referring segmentation in images and videos with natural language. Given an input image (or video) and a referring expression, the goal is to segment the entity referred by the expression in the image or video. In this paper, we propose a cross-modal self-attention (CMSA) module to utilize fine details of individual words and the input image or video, which effectively captures the long-range dependencies between linguistic and visual features. Our model can adaptively focus on informative words in the referring expression and important regions in the visual input. We further propose a gated multi-level fusion (GMLF) module to selectively integrate self-attentive cross-modal features corresponding to different levels of visual features. This module controls the feature fusion of information flow of features at different levels with high-level and low-level semantic information related to different attentive words. find more Besides, we introduce cross-frame self-attention (CFSA) module to effectively integrate temporal information in consecutive frames which extends our method in the case of referring segmentation in videos. Experiments on benchmark datasets of four referring image datasets and two actor and action video segmentation datasets consistently demonstrate that our proposed approach outperforms existing state-of-the-art methods.

Surgical activity recognition is a fundamental step in computer-assisted interventions. This paper reviews the state-of-the-art in methods for automatic recognition of fine-grained gestures in robotic surgery focusing on recent data-driven approaches and outlines the open questions and future research directions.

An article search was performed on 5 bibliographic databases with the following search terms robotic, robot-assisted, JIGSAWS, surgery, surgical, gesture, fine-grained, surgeme, action, trajectory, segmentation, recognition, parsing. Selected articles were classified based on the level of supervision required for training and divided into different groups representing major frameworks for time series analysis and data modelling.

A total of 52 articles were reviewed. The research field is showing rapid expansion, with the majority of articles published in the last 4 years. Deep-learning-based temporal models with discriminative feature extraction and multi-modal data integration have demonstrated promising results on small surgical datasets. Currently, unsupervised methods perform significantly less well than the supervised approaches.

The development of large and diverse open-source datasets of annotated demonstrations is essential for development and validation of robust solutions for surgical gesture recognition. While new strategies for discriminative feature extraction and knowledge transfer, or unsupervised and semi-supervised approaches, can mitigate the need for data and labels, they have not yet been demonstrated to achieve comparable performance. Important future research directions include detection and forecast of gesture-specific errors and anomalies.

This paper is a comprehensive and structured analysis of surgical gesture recognition methods aiming to summarize the status of this rapidly evolving field.

This paper is a comprehensive and structured analysis of surgical gesture recognition methods aiming to summarize the status of this rapidly evolving field.Ankle plantarflexion plays an important role in forward propulsion and anterior-posterior balance during locomotion. This component of gait is often critically impacted by neurotraumas and neurological diseases. We hypothesized that augmenting plantar cutaneous feedback, via closed-loop distal-tibial nerve stimulation, could increase ankle plantarflexion during walking. To test the hypothesis, one intact rat walked on a motorized treadmill with implanted electronic device and electrodes for closed-loop neural recording and stimulation. Constant-current biphasic electrical pulse train was applied to distal-tibial nerve, based on electromyogram recorded from the medial gastrocnemius muscle, to be timed with the stance phase. The stimulation current threshold to evoke plantar cutaneous feedback was set at 30 A (1T), based on compound action potential evoked by stimulation. The maximum ankle joint angle at plantarflexion, during the application of stimulation currents of 3.3T and 6.6T, respectively, was increased from 149.4 (baseline) to 165.4 and 161.6. The minimum ankle joint angle at dorsiflexion was decreased from 59.4 (baseline) to 53.1, during the application of stimulation currents of 3.3T, but not changed by 6.6T. Plantar cutaneous augmentation also changed other gait kinematic parameters. Stance duty factor was increased from 51.9% (baseline) to 65.7% and 64.0%, respectively, by 3.3T and 6.6T, primarily due to a decrease in swing duration. Cycle duration was consistently decreased by the stimulation. In the control trial after two stimulation trials, a strong after-effect was detected in overall gait kinematics as well as ankle plantarflexion, suggesting that this stimulation has the potential for producing long-term changes in gait kinematics.n this paper, we introduce an autonomous robotic ultrasound (US) imaging system based on reinforcement learning (RL). The proposed system and framework are committed to controlling the US probe to perform fully autonomous imaging of a soft, moving and marker-less target based only on single RGB images of the scene.

We propose several different approaches and methods to achieve the following objectives real-time US probe controlling, soft surface constant force tracking and automatic imaging. First, to express the state of the robotic US imaging task, a state representation model is proposed to reduce the dimensionality of the imaging state and encode the force and US information into the scene image space. Then, an RL agent is trained by a policy gradient theorem based RL model with the single RGB image as the only observation. To achieve adaptable constant force tracking between the US probe and the soft moving target, we propose a force-to-displacement control method based on an admittance controller.

In the simulation experiment, we verified the feasibility of the integrated method.