Assessment of the learning curve in health technologies: a systematic review

Objective: We reviewed and appraised the methods by which the issue of the learning curve has been addressed during health technology assessment in the past. Method: We performed a systematic review of papers in clinical databases (BIOSIS, CINAHL, Cochrane Library, EMBASE, HealthSTAR, MEDLINE, Science Citation Index, and Social Science Citation Index) using the search term "learning curve:" Results: The clinical search retrieved 4,571 abstracts for assessment, of which 559 (12%) published articles were eligible for review. Of these, 272 were judged to have formally assessed a learning curve. The procedures assessed were minimal access (51%), other surgical (41%), and diagnostic (8%). The majority of the studies were case series (95%). Some 47% of studies addressed only individual operator performance and 52% addressed institutional performance. The data were collected prospectively in 40%, retrospectively in 26%, and the method was unclear for 31%. The statistical methods used were simple graphs (44%), splitting the data chronologically and performing a t test or chi-squared test (60%), curve fitting (12%), and other model fitting (5%). Conclusions: Learning curves are rarely considered formally in health technology assessment. Where they are, the reporting of the studies and the statistical methods used are weak. As a minimum, reporting of learning should include the number and experience of the operators and a detailed description of data collection. Improved statistical methods would enhance the assessment of health technologies that require learning

Assessing the learning curve effect in health technologies: Lessons from the non-clinical literature

Introduction: Many health technologies exhibit some form of learning effect, and this represents a barrier to rigorous assessment. It has been shown that the statistical methods used are relatively crude. Methods to describe learning curves in fields outside medicine, for example, psychology and engineering, may be better. Methods: To systematically search non–health technology assessment literature (for example, PsycLit and Econlit databases) to identify novel statistical techniques applied to learning curves. Results: The search retrieved 9,431 abstracts for assessment, of which 18 used a statistical technique for analyzing learning effects that had not previously been identified in the clinical literature. The newly identified methods were combined with those previously used in health technology assessment, and categorized into four groups of increasing complexity: a) exploratory data analysis; b) simple data analysis; c) complex data analysis; and d) generic methods. All the complex structured data techniques for analyzing learning effects were identified in the nonclinical literature, and these emphasized the importance of estimating intra- and interindividual learning effects. Conclusion: A good dividend of more sophisticated methods was obtained by searching in nonclinical fields. These methods now require formal testing on health technology data sets

Dynamic modeling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

This article concerns the problem of dynamic modeling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual-manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model orientated research using the same machine, the article develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimize the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivization of an output error single performance index. The developed algorithm utilises a multi-objective Genetic Algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of ‘true’ parameters) and experimental data. Both simulation and experimental results show that multi-objectivization has improved convergence of the estimated parameters compared to the single objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem

A vision-based positioning system with inverse dead-zone control for dual-hydraulic manipulators

The robotic platform in this study is being used for research into assisted tele-operation for common nuclear decommissioning tasks, such as remote pipe cutting. It has dual, seven-function, hydraulically actuated manipulators mounted on a mobile base unit. For the new visual servoing system, the user selects an object from an on-screen image, whilst the computer control system determines the required position and orientation of the manipulators; and controls the joint angles for one of these to grasp the pipe and the second to position for a cut. Preliminary testing shows that the new system reduces task completion time for both inexperienced and experienced operators, in comparison to tele-operation. In a second contribution, a novel state-dependent parameter (SDP) control system is developed, for improved resolved motion of the manipulators. Compared to earlier SDP analysis of the same device, which used a rather ad hoc scaling method to address the dead-zone, a state-dependent gain is used to implement inverse dead-zone control. The new approach integrates input signal calibration, system identification and nonlinear control design, allowing for straightforward recalibration when the dynamic characteristics have changed or the actuators have deteriorated due to age

A genetic algorithm approach for parameter optimization of a 7DOF robotic manipulator

In this paper the problem of dynamic modeling and parameter estimation of a seven degree of freedom hydraulic manipulator is investigated. The numerical model is developed in Simulink using SimMechanic and Simscape toolboxes with unknown/uncertain parameters. The aim of this paper is to develop a mechanism that enables us to find a feasible set of parameters for the robot that is consistent with measurements of the input, output, and states of the system under noisy and unknown operating conditions. As the first step a genetic algorithm is developed to solve an output error system identification problem for a specific joint, i.e. joint 2, such that the parameters of the joint converge to the desired set of parameters within an acceptable accuracy. The results can be straightforwardly extended to all joints of the manipulato

A new approach to improve the parameter estimation accuracy in robotic manipulators using a multi-objective output error identification technique

The research behind this article primarily concerns the development of mobile robots for nuclear decommissioning. The robotic platform under study has dual, seven-function, hydraulically actuated manipulators, for which the authors have developed a vision based, assisted teleoperation interface for common decommissioning tasks such as pipe cutting. However, to improve safety, task execution speed and operator training-time, high performance control of the nonlinear manipulator dynamics is required. Hence, the present article focuses on an associated dynamic model, and addresses the challenging generic task of parameter estimation for a highly convex and nonlinear system. A novel approach for estimation of the fundamental parameters of the manipulator, based on the idea of multi-objectivization, is proposed. Here, a single objective output error identification problem is converted into a multi-objective optimization problem. This is solved using a multi-objective genetic algorithm with non-dominated sorting. Numerical and experimental results using the nuclear decommissioning robot, show that the performance of the proposed approach, in terms of both the output error index and the accuracy of the estimated parameters, is superior to the previously studied single-objective identification problem

Towards a Cooperative Robotic System for Autonomous Pipe Cutting in Nuclear Decommissioning

A mobile camera is used to support an assisted teleoperation pipe–cutting system for nuclear decommissioning. The base system consists of dual–manipulators with a single mounted Kinect camera. The user selects the object from an on–screen image, whilst the computer control system automatically grasps the pipe with one end–effector and positions the second for cutting. However, the system fails in some cases because of data limitations, for example a partially obscured pipe in a challenging decommissioning scenario (simulated in the laboratory). Hence, the present article develops a new method to increase the use case scenarios via the introduction of mobile cameras e.g. for mounting on a drone. This is a non-trivial problem, with SLAM and ArUco fiducials introduced to locate the cameras, and a novel error correction method proposed for finding the ArUco markers. Preliminary results demonstrate the validity of the approach but improvements will be required for robust autonomous cutting. Hence, to reduce the pipe position estimation errors, suggestions are made for various algorithmic and hardware refinements

A Low-Cost and Semi-Autonomous Robotic Scanning System for Characterising Radiological Waste

A novel, semi-autonomous radiological scanning system for inspecting irregularly shaped and radiologically uncharacterised objects in various orientations is presented. The system utilises relatively low cost, commercial-off-the-shelf (COTS) electronic components, and is intended for use within relatively low to medium radioactive dose environments. To illustrate the generic concepts, the combination of a low-cost COTS vision system, a six DoF manipulator and a gam-ma radiation spectrometer are investigated. Three modes of vision have been developed, al-lowing a remote operator to choose the most appropriate algorithm for the task. The robot arm subsequently scans autonomously across the selected object, determines the scan positions and enables the generation of radiological spectra using the gamma spectrometer. These data inform the operator of any likely radioisotopes present, where in the object they are located and thus whether the object should be treated as LLW, ILW or HLW