P. Aqueveque, Manuel Gutiérrez, Guisella Peña, Enrique I. Germany, Britam Gómez, Gustavo Retamal, P. Ortega-Bastidas
{"title":"上肢肌肉骨骼疾病重复动作风险评估的自动平台-初步结果。","authors":"P. Aqueveque, Manuel Gutiérrez, Guisella Peña, Enrique I. Germany, Britam Gómez, Gustavo Retamal, P. Ortega-Bastidas","doi":"10.1109/MeMeA57477.2023.10171943","DOIUrl":null,"url":null,"abstract":"Work-related musculoskeletal disorders is a major problem for worker’s health when dealing with repetitive tasks. The Occupational Repetitive Actions Index (OCRA) is one of the most widely used methods for determining upper extremity risk from repetitive actions. Traditional risk assessment consists in observing and recording the entire workday or part of it. This observational assessment depends on the evaluator’s expertise, which lead to inter-evaluator variability in the results even for the same job. This paper presents preliminary results of a platform that combines motion capture data with a digitalized OCRA Index method to classify risks under repetitive tasks conditions. Repetitive action tasks were performed in a controlled laboratory environment on ten healthy subjects, where optical and inertial sensors were used to capture movement. Body segment positions, displacements and joint angles were fed into the platform comparing their processing time and reliability. Although results indicate that optical systems have a better performance than inertial sensors for posture evaluation, the last has better practical applicability since optical markers occlusion in the working setup induced measurement errors. In addition, inertial systems are more likely to be used on in real working scenarios due to their ease of use and portability. Finally, inertial sensors combined with the digitized OCRA index method platform effectively reduces 67% of the needed evaluation time compared to traditional observational methods.","PeriodicalId":191927,"journal":{"name":"2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automatic Platform for Upper Extremity Musculoskeletal Disorder Risks Estimation from Repetitive Actions -Preliminary results.\",\"authors\":\"P. Aqueveque, Manuel Gutiérrez, Guisella Peña, Enrique I. Germany, Britam Gómez, Gustavo Retamal, P. Ortega-Bastidas\",\"doi\":\"10.1109/MeMeA57477.2023.10171943\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Work-related musculoskeletal disorders is a major problem for worker’s health when dealing with repetitive tasks. The Occupational Repetitive Actions Index (OCRA) is one of the most widely used methods for determining upper extremity risk from repetitive actions. Traditional risk assessment consists in observing and recording the entire workday or part of it. This observational assessment depends on the evaluator’s expertise, which lead to inter-evaluator variability in the results even for the same job. This paper presents preliminary results of a platform that combines motion capture data with a digitalized OCRA Index method to classify risks under repetitive tasks conditions. Repetitive action tasks were performed in a controlled laboratory environment on ten healthy subjects, where optical and inertial sensors were used to capture movement. Body segment positions, displacements and joint angles were fed into the platform comparing their processing time and reliability. Although results indicate that optical systems have a better performance than inertial sensors for posture evaluation, the last has better practical applicability since optical markers occlusion in the working setup induced measurement errors. In addition, inertial systems are more likely to be used on in real working scenarios due to their ease of use and portability. Finally, inertial sensors combined with the digitized OCRA index method platform effectively reduces 67% of the needed evaluation time compared to traditional observational methods.\",\"PeriodicalId\":191927,\"journal\":{\"name\":\"2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MeMeA57477.2023.10171943\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MeMeA57477.2023.10171943","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic Platform for Upper Extremity Musculoskeletal Disorder Risks Estimation from Repetitive Actions -Preliminary results.
Work-related musculoskeletal disorders is a major problem for worker’s health when dealing with repetitive tasks. The Occupational Repetitive Actions Index (OCRA) is one of the most widely used methods for determining upper extremity risk from repetitive actions. Traditional risk assessment consists in observing and recording the entire workday or part of it. This observational assessment depends on the evaluator’s expertise, which lead to inter-evaluator variability in the results even for the same job. This paper presents preliminary results of a platform that combines motion capture data with a digitalized OCRA Index method to classify risks under repetitive tasks conditions. Repetitive action tasks were performed in a controlled laboratory environment on ten healthy subjects, where optical and inertial sensors were used to capture movement. Body segment positions, displacements and joint angles were fed into the platform comparing their processing time and reliability. Although results indicate that optical systems have a better performance than inertial sensors for posture evaluation, the last has better practical applicability since optical markers occlusion in the working setup induced measurement errors. In addition, inertial systems are more likely to be used on in real working scenarios due to their ease of use and portability. Finally, inertial sensors combined with the digitized OCRA index method platform effectively reduces 67% of the needed evaluation time compared to traditional observational methods.