Muscle activation during traditional laparoscopic surgery compared with robot-assisted laparoscopic surgery: a meta-analysis.

BACKGROUND: The repetitive and forceful motions used by operating surgeons increase the risk of developing musculoskeletal disorders. Most ergonomists consider the surgical environment to be incredibly harsh for its workers. Traditional Laparoscopic Surgery (TLS) in particular has a number of physical and mental challenges associated with it, and while Robotic-Assisted Laparoscopic Surgery (RALS) provides several features that improve upon TLS, some surgeons have still reported musculoskeletal symptoms they attribute to RALS. In this paper, we endeavored to systematically review muscle activation for both TLS and RALS, to compare the modalities and present the results as a meta-analysis. METHODS: A literature search was conducted using Pubmed, Embase, and Cochrane databases in November 2018 with the following inclusion criteria: full text was available in English, the paper contained original data, EMG was one of the primary measurement techniques, and the paper included EMG data for both TLS and RALS. Results from studies were compared using standardized mean difference analysis. RESULTS: A total of 379 papers were found, and through screening ten were selected for inclusion. Sample populations ranged from 1 to 31 surgeons, and a variety of study designs and metrics were used between studies. The biceps were the only muscle group that consistently and significantly demonstrated lower muscle activation for RALS for all included studies. CONCLUSIONS: The results may support the belief that RALS is ergonomically superior to TLS, shown through generally lower muscle activation scores. However, these results must be interpreted with caution due to the heterogeneity between the studies and multiple potential sources for bias within studies. This analysis would be strengthened with a higher number of homogenous, high-quality studies examining larger sample sizes.

Particle Swarm Optimization approach to defect detection in armour ceramics.

In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function.

Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%.

Ultrasonic sensor based defect detection and characterisation of ceramics.

Ceramic tiles, used in body armour systems, are currently inspected visually offline using an X-ray technique that is both time consuming and very expensive. The aim of this research is to develop a methodology to detect, locate and classify various manufacturing defects in Reaction Sintered Silicon Carbide (RSSC) ceramic tiles, using an ultrasonic sensing technique. Defects such as free silicon, un-sintered silicon carbide material and conventional porosity are often difficult to detect using conventional X-radiography. An alternative inspection system was developed to detect defects in ceramic components using an Artificial Neural Network (ANN) based signal processing technique. The inspection methodology proposed focuses on pre-processing of signals, de-noising, wavelet decomposition, feature extraction and post-processing of the signals for classification purposes. This research contributes to developing an on-line inspection system that would be far more cost effective than present methods and, moreover, assist manufacturers in checking the location of high density areas, defects and enable real time quality control, including the implementation of accept/reject criteria.

Uncertainty estimation using fuzzy measures for multiclass classification.

Uncertainty arises in classification problems when the input pattern is not perfect or measurement error is unavoidable. In many applications, it would be beneficial to obtain an estimate of the uncertainty associated with a new observation and its membership within a particular class. Although statistical classification techniques base decision boundaries according to the probability distributions of the patterns belonging to each class, they are poor at supplying uncertainty information for new observations. Previous research has documented a multiarchitecture, monotonic function neural network model for the representation of uncertainty associated with a new observation for two-class classification. This paper proposes a modification to the monotonic function model to estimate the uncertainty associated with a new observation for multiclass classification. The model, therefore, overcomes a limitation of traditional classifiers that base decisions on sharp classification boundaries. As such, it is believed that this method will have advantages for applications such as biometric recognition in which the estimation of classification uncertainty is an important issue. This approach is based on the transformation of the input pattern vector relative to each classification class. Separate, monotonic, single-output neural networks are then used to represent the "degree-of-similarity" between each input pattern vector and each class. An algorithm for the implementation of this approach is proposed and tested with publicly available face-recognition data sets. The results indicate that the suggested approach provides similar classification performance to conventional principle component analysis (PCA) and linear discriminant analysis (LDA) techniques for multiclass pattern recognition problems as well as providing uncertainty information caused by misclassification.