DOI: 10.57760/sciencedb.26030
title: Human Injury Risk Prediction of Large Equipment Confined Space Operations
title: 大型设备受限空间作业人员伤害风险预测

description: There are two folders in total, folder 1 is named "Comprehensive Assessment of Human Joint Injury Risk" and folder 2 is named "Prediction of Human Joint Injury Risk". The folder "Comprehensive Assessment of Human Joint Injury Risk" contains two code files, "fuzzy method. py" (6KB) and "robustness check of fuzzy functions" (10KB), one table file "data. xlsx" (137KB), and one txt instruction file "readme. txt". The code data is all running on Python 3.11. The table file "data. xlsx" consists of 980 rows and 13 columns, representing 980 pieces of data. Each piece of data is composed of 13 sub-factors, and the "fuzzy method" file can be used to obtain the comprehensive assessment results of joint risk. The robustness of the "robustness check of fuzzy functions" verifiable method is adopted. The 13 sub-factors represent the risk index of injury to various joints in the human body (calculated by the ratio of joint load to safe load values), namely right wrist injury risk index, left wrist injury risk index, right elbow injury risk index, left elbow injury risk index, right shoulder injury risk index, left shoulder injury risk index, right hip injury risk index, left hip injury risk index, right knee injury risk index, left knee injury risk index, right ankle injury risk index, left ankle injury risk index, and lower back injury risk index. The folder "Prediction of Human Joint Injury Risk" contains a code file, "prediction model. py" (3KB), two table file“data for risk prediction.xls” (310KB), “data for risk prediction_one hot coding.xls”(350KB), and a text description file "readme. txt".The code data is all running on Python 3.11. The table file "data for risk prediction. xls" consists of 980 rows and 26 columns, representing 980 pieces of data. The first column represents the sample number, the second column represents which posture category the current sample belongs to (1-9 represent 9 postures), the third column represents human height, the fourth column represents hand workload, and the fifth to seventeenth columns represent the risk index of various joint injuries in the human body (calculated by the ratio of joint load to safety load value). The 18th column represents the comprehensive injury risk level (three levels in total: 1, 2, 3). The table "data for risk prediction_one hot coding.xls" contains data where the posture categories are one-hot encoded, based on the "data for risk prediction.xls".Based on the “data for risk prediction_one hot coding.xls” data, SPSS software can be used for multiple logistic regression modeling (Posture category, height, and load are dependent variables, and the comprehensive injury risk level is a factor）. Based on the results of the multiple logistic regression modeling, a "prediction model. py" program was designed to visually present the prediction results. 
description: There are two folders in total, folder 1 is named "Comprehensive Assessment of Human Joint Injury Risk" and folder 2 is named "Prediction of Human Joint Injury Risk". The folder "Comprehensive Assessment of Human Joint Injury Risk" contains two code files, "fuzzy method. py" (6KB) and "robustness check of fuzzy functions" (10KB), one table file "data. xlsx" (137KB), and one txt instruction file "readme. txt". The code data is all running on Python 3.11. The table file "data. xlsx" consists of 980 rows and 13 columns, representing 980 pieces of data. Each piece of data is composed of 13 sub-factors, and the "fuzzy method" file can be used to obtain the comprehensive assessment results of joint risk. The robustness of the "robustness check of fuzzy functions" verifiable method is adopted. The 13 sub-factors represent the risk index of injury to various joints in the human body (calculated by the ratio of joint load to safe load values), namely right wrist injury risk index, left wrist injury risk index, right elbow injury risk index, left elbow injury risk index, right shoulder injury risk index, left shoulder injury risk index, right hip injury risk index, left hip injury risk index, right knee injury risk index, left knee injury risk index, right ankle injury risk index, left ankle injury risk index, and lower back injury risk index. The folder "Prediction of Human Joint Injury Risk" contains a code file, "prediction model. py" (3KB), two table file“data for risk prediction.xls” (310KB), “data for risk prediction_one hot coding.xls”(350KB), and a text description file "readme. txt".The code data is all running on Python 3.11. The table file "data for risk prediction. xls" consists of 980 rows and 26 columns, representing 980 pieces of data. The first column represents the sample number, the second column represents which posture category the current sample belongs to (1-9 represent 9 postures), the third column represents human height, the fourth column represents hand workload, and the fifth to seventeenth columns represent the risk index of various joint injuries in the human body (calculated by the ratio of joint load to safety load value). The 18th column represents the comprehensive injury risk level (three levels in total: 1, 2, 3). The table "data for risk prediction_one hot coding.xls" contains data where the posture categories are one-hot encoded, based on the "data for risk prediction.xls".Based on the “data for risk prediction_one hot coding.xls” data, SPSS software can be used for multiple logistic regression modeling (Posture category, height, and load are dependent variables, and the comprehensive injury risk level is a factor）. Based on the results of the multiple logistic regression modeling, a "prediction model. py" program was designed to visually present the prediction results.