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使用计算机算法自动识别美国政府工业卫生学家会议阈限值的十二个提升区相对于三个简化区的准确性。

Accuracy of Automatically Identifying the American Conference of Governmental Industrial Hygienists Threshold Limit Values Twelve Lifting Zones over Three Simplified Zones Using Computer Algorithm.

作者信息

Barim Menekse S, Lu Ming-Lun, Feng Shuo, Hayden Marie A, Werren Dwight

机构信息

National Institute for Occupational Safety and Health, Cincinnati, OH 45226, USA.

Meta, San Jose, CA 94025, USA.

出版信息

Sensors (Basel). 2024 Dec 27;25(1):111. doi: 10.3390/s25010111.

DOI:10.3390/s25010111
PMID:39796902
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11722830/
Abstract

The American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Values (TLVs) for lifting provides risk zones for assessing two-handed lifting tasks. This paper describes two computational models for identifying the lifting risk zones using gyroscope information from five inertial measurement units (IMUs) attached to the lifter. Two models were developed: (1) the ratio model using body segment length ratios of the forearm, upper arm, trunk, thigh, and calf segments, and (2) the ratio + length model using actual measurements of the body segments in the ratio model. The models were evaluated using data from 360 lifting trials performed by 10 subjects (5 males and 5 females) with an average age of 51.50 (±9.83) years. The accuracy of the two models was compared against data collected by a laboratory-based motion capture system as a function of 12 ACGIH lifting risk zones and 3 grouped risk zones (low, medium, and high). Results showed that only the ratio + length model provides acceptable estimates of lifting risk with an average of 69% accuracy level for predicting one of the 3 grouped zones and a higher rate of 92% for predicting the high lifting zone.

摘要

美国政府工业卫生学家会议(ACGIH)的搬运阈限值(TLVs)提供了用于评估双手搬运任务的风险区域。本文描述了两种计算模型,用于利用附着在搬运者身上的五个惯性测量单元(IMU)的陀螺仪信息来识别搬运风险区域。开发了两种模型:(1)使用前臂、上臂、躯干、大腿和小腿节段的身体节段长度比的比例模型,以及(2)使用比例模型中身体节段实际测量值的比例+长度模型。使用来自10名受试者(5名男性和5名女性)进行的360次搬运试验的数据对模型进行评估,受试者平均年龄为51.50(±9.83)岁。将这两种模型的准确性与基于实验室的动作捕捉系统收集的数据进行比较,该数据是关于12个ACGIH搬运风险区域和3个分组风险区域(低、中、高)的函数。结果表明,只有比例+长度模型能够提供可接受的搬运风险估计,预测3个分组区域之一的平均准确率为69%,预测高搬运风险区域的准确率更高,为92%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d03/11722830/29a1f9ca33d2/sensors-25-00111-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d03/11722830/bbed978de5be/sensors-25-00111-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d03/11722830/29a1f9ca33d2/sensors-25-00111-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d03/11722830/aa837bc266d9/sensors-25-00111-g004.jpg
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