Accuracy of Automatically Identifying the American Conference of Governmental Industrial Hygienists Threshold Limit Values Twelve Lifting Zones over Three Simplified Zones Using Computer Algorithm
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 inert...
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/111 |
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| author | Menekse S. Barim Ming-Lun Lu Shuo Feng Marie A. Hayden Dwight Werren |
| author_facet | Menekse S. Barim Ming-Lun Lu Shuo Feng Marie A. Hayden Dwight Werren |
| author_sort | Menekse S. Barim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b5981108a5c542b6ac2ec68ff1999a68 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-b5981108a5c542b6ac2ec68ff1999a682025-01-10T13:20:54ZengMDPI AGSensors1424-82202024-12-0125111110.3390/s25010111Accuracy of Automatically Identifying the American Conference of Governmental Industrial Hygienists Threshold Limit Values Twelve Lifting Zones over Three Simplified Zones Using Computer AlgorithmMenekse S. Barim0Ming-Lun Lu1Shuo Feng2Marie A. Hayden3Dwight Werren4National Institute for Occupational Safety and Health, Cincinnati, OH 45226, USANational Institute for Occupational Safety and Health, Cincinnati, OH 45226, USAMeta, San Jose, CA 94025, USANational Institute for Occupational Safety and Health, Cincinnati, OH 45226, USANational Institute for Occupational Safety and Health, Cincinnati, OH 45226, USAThe 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.https://www.mdpi.com/1424-8220/25/1/111ACGIH TLVS for liftingIMUliftingcomputer algorithm |
| spellingShingle | Menekse S. Barim Ming-Lun Lu Shuo Feng Marie A. Hayden Dwight Werren Accuracy of Automatically Identifying the American Conference of Governmental Industrial Hygienists Threshold Limit Values Twelve Lifting Zones over Three Simplified Zones Using Computer Algorithm Sensors ACGIH TLVS for lifting IMU lifting computer algorithm |
| title | Accuracy of Automatically Identifying the American Conference of Governmental Industrial Hygienists Threshold Limit Values Twelve Lifting Zones over Three Simplified Zones Using Computer Algorithm |
| title_full | Accuracy of Automatically Identifying the American Conference of Governmental Industrial Hygienists Threshold Limit Values Twelve Lifting Zones over Three Simplified Zones Using Computer Algorithm |
| title_fullStr | Accuracy of Automatically Identifying the American Conference of Governmental Industrial Hygienists Threshold Limit Values Twelve Lifting Zones over Three Simplified Zones Using Computer Algorithm |
| title_full_unstemmed | Accuracy of Automatically Identifying the American Conference of Governmental Industrial Hygienists Threshold Limit Values Twelve Lifting Zones over Three Simplified Zones Using Computer Algorithm |
| title_short | Accuracy of Automatically Identifying the American Conference of Governmental Industrial Hygienists Threshold Limit Values Twelve Lifting Zones over Three Simplified Zones Using Computer Algorithm |
| title_sort | accuracy of automatically identifying the american conference of governmental industrial hygienists threshold limit values twelve lifting zones over three simplified zones using computer algorithm |
| topic | ACGIH TLVS for lifting IMU lifting computer algorithm |
| url | https://www.mdpi.com/1424-8220/25/1/111 |
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