Leveraging machine learning for predicting human body model response in restraint design simulations

Joodaki, Hamed; Gepner, Bronisław; Kerrigan, Jason

doi:10.1080/10255842.2020.1841754

Cited by 11 publications

(16 citation statements)

References 25 publications

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“…Elastic net regression is an extension of the ridge regression and least absolute shrinkage and selection operator (LASSO) regression that combines the regularization techniques of each method and has been shown to have superior performance [19]. Regularized regression models such as elastic net or LASSO have been increasingly used to perform feature selection and assess which variables are most important for a desired biomechanical outcome [20,21]. By introducing a penalization parameter, this model was capable of testing every kinematic variable available, selecting out the ones that were not predictive, there by improving the accuracy of the model predictions.…”

Section: Methodsmentioning

confidence: 99%

Kinematic Models For Pitch Location Metrics in Professional Baseball Pitchers

Manzi¹,

Dowling²,

Spencer³

et al. 2021

Arch Sports Med

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Prior studies limit their evaluations to the distal extremity, without considering the entirety of the pitching motion or the position of the pitcher relative to the intended target. In other over arm throwing sports such as cricket, in accurate pitches were noted to significantly differ by way of lower maximum lead knee lift, decreased knee extension in the arm acceleration phase, significantly less elbow flexion

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Section: Methodsmentioning

confidence: 99%

Kinematic Models For Pitch Location Metrics in Professional Baseball Pitchers

Manzi¹,

Dowling²,

Spencer³

et al. 2021

Arch Sports Med

View full text Add to dashboard Cite

show abstract

“…SVR is a supervised ML model leveraged for regression and classification analysis. [ 41 ] Despite its origins as a classification system, the support vector machine was extended to include regression later. By minimizing the difference between predicted and observed values, SVR fits and reduces the error within a certain threshold.…”

Section: Models Employedmentioning

confidence: 99%

Predicting the compressive strength of additively manufactured PLA‐based orthopedic bone screws: A machine learning framework

2022

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Additive manufacturing (AM) technology is an innovative technique that hasshown potential in several surgical innovations such as the fabrication of costeffective orthopedic screws. It is imperative that the process parameters used during the fabrication of bone screws determine the strength of the screw to support the load-bearing fracture sites. In this present work, the application of machine learning (ML) models was leveraged for predicting the compressive strength of additively manufactured orthopedic cortical screws. Different ML models such as k-nearest neighbors, support vector regression, decision trees, and random forest were compared to offer a robust predictive ML model. During the study, fused deposition modeling based additive manufacturing technology is used to fabricate poly(lactic acid) based cortical screws and compressive strength was monitored using the universal testing machine. The predictive ML models were developed by various independent parameters such as infill percentage, layer height, infill pattern and wall thickness. The adequacy and robustness of different ML models were observed at different error metrics. The error metrics of the random forest model were comparatively lower for testing data with a mean absolute error of 2.06 ± 0.91, root mean square error of 2.37 ± 0.9 and coefficient of determination of 0.96 ± 0.042. The results highlight that the random forest is the most adequate ML model for predicting the compressive strength of additively manufactured cortical screws.additive manufacturing, bone screw, compressive strength, machine learning | INTRODUCTIONAdditive manufacturing (AM) technology provides the ideal tailor-made solution for different orthopedic applications. [1] This technology offers various merits and functionalities such as precision, [2] ease of the process, [3] less material wastage, [4] personalization, [5] degree of freedom, [6] accuracy, [7] biocompatibility, [8] and customization [9] for the

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“…In today's big data era, computer technology has developed rapidly. The research of machine learning combined with big data has played a more and more important role in the development and competition of various fields [19][20][21]. Applying machine learning to infectious disease early warning system can break through the limitations of traditional prediction system and achieve the purpose of real-time and dynamic early warning, thereby improving the effect of prevention and control [22,23].…”

Section: Related Work 21 Covid-19 Data Set Source and Analysismentioning

confidence: 99%

Research on Forecast of Global COVID-19 Development Trend

Cheng

Bai

et al. 2021

Preprint

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The rapid spread of Corona Virus Disease 2019 (COVID-2019) has seriously threatened people’s health and brought huge challenges to the medical systems of many countries. So it is necessary to predict the epidemic trend scientifically and accurately. In this paper, the development trend prediction of the global daily cumulative confirmed case, based on data from February 2, 2020 to August 14, 2021 using linear regression and nonlinear regression methods, which are auto regressive integrated moving average (ARIMA), wavelet neural network (WNN), support vector machine (SVM), recurrent neural network (RNN) and long short-term memory (LSTM). Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to evaluate the prediction accuracy of the five models. The experimental results showed that the LSTM used in this study is more accurate for predicting the development trend of global cumulative confirmed cases. And its three evaluation indexes were 0.0936%, 0.1123%, and 0.0962% respectively, which were the smallest compared with the other four models. The LSTM model was used to predict the global cumulative confirmed cases in the next five days. The prediction results showed that the global cumulative confirmed cases will rise steadily and exceed 205 million. Therefore, the health departments of various countries should take appropriate prevention and control measures in advance.

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Leveraging machine learning for predicting human body model response in restraint design simulations

Cited by 11 publications

References 25 publications

Kinematic Models For Pitch Location Metrics in Professional Baseball Pitchers

Kinematic Models For Pitch Location Metrics in Professional Baseball Pitchers

Predicting the compressive strength of additively manufactured PLA‐based orthopedic bone screws: A machine learning framework

Research on Forecast of Global COVID-19 Development Trend

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