Assessing low-back loading during lifting using personalized electromyography-driven trunk models and NIOSH-based risk levels
Workplace injury risk due to physically demanding tasks (e.g., repeated lifting) is currently assessed using ergonomic guidelines. The Revised NIOSH Lifting Equation (RNLE) is a commonly used approach that assesses risk of low-back loading during different lifting tasks. Advances in musculoskeletal...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1486931/full |
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| Summary: | Workplace injury risk due to physically demanding tasks (e.g., repeated lifting) is currently assessed using ergonomic guidelines. The Revised NIOSH Lifting Equation (RNLE) is a commonly used approach that assesses risk of low-back loading during different lifting tasks. Advances in musculoskeletal models have enabled the estimation of physiologically valid person-specific musculoskeletal models (pEMS) driven by surface electromyography and joint angle information. These models offer realistic estimates of objective parameters such as moments and compressive and shear loads at the lumbosacral joint. In this study, we applied both techniques (RNLE and pEMS) to assess risk and low-back loading in seven healthy participants performing lifting tasks at different risk levels. We found that the pEMS estimated objective parameters of low-back loading in line with the different risk levels proposed by RNLE. However, the low-back compressive and shear loads were higher than the limits proposed by the RNLE. Moreover, we show that the lumbosacral compressive loads can be a better parameter to demarcate risk levels. We recommend performing this assessment on a larger and diverse population for evaluation of personalized risk levels across lifting tasks in the industry. These approaches can be implemented with wearable sensorized garments to monitor personalized musculoskeletal health unobtrusively in the workplace providing us a better insight into possibility of individual risk. |
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| ISSN: | 2296-4185 |