Immersive virtual reality as an empirical research tool: exploring the capability of a machine learning model for predicting construction workers’ safety behaviour

2021 ◽  
Author(s):  
Yifan Gao ◽  
Vicente A. González ◽  
Tak Wing Yiu ◽  
Guillermo Cabrera-Guerrero ◽  
Nan Li ◽  
...  
Author(s):  
Thomas Stranick ◽  
Christian Lopez

Abstract This work presents a Virtual Reality Exergame application designed to prevent Work Related Musculoskeletal Disorders (WMSDs). Moreover, to help adapt the tasks of the exergame, a machine learning model that predicts users' exercise intensity level is presented. WMSDs are an important issue that can have a direct economic impact to an organization. Exercise and stretching is one method that can benefit workers and help prevent WMSDs. While several applications have been developed to prevent WMSDs, most of them suffer from a lack of immersivity or they just focus on education and not necessarily on helping workers warm up or stretch. In light of this, an Exergame application that leverages VR and Depth-sensor technology to help provide users with an immersive first-person experience that engage them in physical activities is introduced in this work. The objective of the Exergame is to motivate users to perform full-body movements in order to pass through a series of obstacles. While in the game, users can visualize their motions by controlling the virtual avatar with their body movements. It is expected that this immersivity will motivate and encourage the users. Initial findings show the positive effects that the base exergame has on individuals' motivation and physical activity. The results indicate that the application was able to engage individuals in low-intensity exercises that produced significant and consistent increases in their heart rate. Lastly, the results show that the machine learning model predicted users' exercise activity level with an accuracy of 76.67%.


2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.


Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.


Author(s):  
Dhaval Patel ◽  
Shrey Shrivastava ◽  
Wesley Gifford ◽  
Stuart Siegel ◽  
Jayant Kalagnanam ◽  
...  

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