Predicting Mechanical Properties of Galvanized Steels: Data Mining Approach

The purpose of this paper is to predict the mechanical properties of galvanized steel, using appropriate data mining techniques such as neural network, support vector machine, regression analysis and regression tree methods. It is found that by using the neural network technique one can get the best result for predicting the mechanical properties of galvanized steel according to the values of input parameters and also considering the effects of annealing temperature and line speed as the controlling parameters

Multi-Task Recurrent Neural Network for Surgical Gesture Recognition and Progress Prediction

Surgical gesture recognition is important for surgical data science and computer-aided intervention. Even with robotic kinematic information, automatically segmenting surgical steps presents numerous challenges because surgical demonstrations are characterized by high variability in style, duration and order of actions. In order to extract discriminative features from the kinematic signals and boost recognition accuracy, we propose a multi-task recurrent neural network for simultaneous recognition of surgical gestures and estimation of a novel formulation of surgical task progress. To show the effectiveness of the presented approach, we evaluate its application on the JIGSAWS dataset, that is currently the only publicly available dataset for surgical gesture recognition featuring robot kinematic data. We demonstrate that recognition performance improves in multi-task frameworks with progress estimation without any additional manual labelling and training.Comment: Accepted to ICRA 202

Autonomous Camera Movement for Robotic-Assisted Surgery: A Survey

In the past decade, Robotic-Assisted Surgery (RAS) has become a widely accepted technique as an alternative to traditional open surgery procedures. The best robotic assistant system should combine both human and robot capabilities under the human control. As a matter of fact robot should collaborate with surgeons in a natural and autonomous way, thus requiring less of the surgeons\u27 attention. In this survey, we provide a comprehensive and structured review of the robotic-assisted surgery and autonomous camera movement for RAS operation. We also discuss several topics, including but not limited to task and gesture recognition, that are closely related to robotic-assisted surgery automation and illustrate several successful applications in various real-world application domains. We hope that this paper will provide a more thorough understanding of the recent advances in camera automation in RSA and offer some future research directions

Distance‐based time series classification approach for task recognition with application in surgical robot autonomy

BackgroundRobotic‐assisted surgery allows surgeons to perform many types of complex operations with greater precision than is possible with conventional surgery. Despite these advantages, in current systems, a surgeon should communicate with the device directly and manually. To allow the robot to adjust parameters such as camera position, the system needs to know automatically what task the surgeon is performing.MethodsA distance‐based time series classification framework has been developed which measures dynamic time warping distance between temporal trajectory data of robot arms and classifies surgical tasks and gestures using a k‐nearest neighbor algorithm.ResultsResults on real robotic surgery data show that the proposed framework outperformed state‐of‐the‐art methods by up to 9% across three tasks and by 8% across gestures.ConclusionThe proposed framework is robust and accurate. Therefore, it can be used to develop adaptive control systems that will be more responsive to surgeons’ needs by identifying next movements of the surgeon. Copyright © 2016 John Wiley & Sons, Ltd.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138333/1/rcs1766.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138333/2/rcs1766_am.pd

Automated robot‐assisted surgical skill evaluation: Predictive analytics approach

BackgroundSurgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot‐assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise.MethodsEight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise – novice and expert. Three classification methods – k‐nearest neighbours, logistic regression and support vector machines – are applied.ResultsThe result shows that the proposed framework can classify surgeons’ expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task.ConclusionThis study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141457/1/rcs1850.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141457/2/rcs1850_am.pd

CLUSTERING SURGEMES USING PROTOTYPES FROM ROBOTIC KINEMATIC INFORMATION

Training a surgeon to be skilled and competent to perform a given surgical procedure is essential in providing a high quality of care and reducing the risk of complications. However, existing training techniques limit us from conducting in-depth analyses of surgical motions to evaluate these skills accurately. We develop a method to identify the gestures by applying unsupervised methods to cluster the surgical activities learned directly from raw kinematic data. We design an unsupervised method to determine the surgical motions in a Suturing procedure based on predefined surgical gestures. The first step is to find the prototypes by clustering the surgemes of the expert surgeon from all the same expert trials. Then, we map the other surgeons surgemes to the nearest representative of the prototypes and report the clustering accuracy by employing the rand index technique. We utilize four techniques in our proposed unsupervised approach for gesture clustering based on Hierarchical and FCM algorithms. In addition, we highlight the advantages of representing time series data before clustering in terms of computation time saving and system complexity reduction, respectively