When performing lifting tasks at work, the Lifting Index () is widely used to prevent work-related low-back disorders, but it presents criticalities pertaining to measurement accuracy and precision. Wearable sensor networks, such as sensorized insoles and inertial measurement units, could improve biomechanical risk assessment by enabling the computation of an adaptive () that changes over time in relation to the actual method of carrying out lifting. This study aims to illustrate the concepts and mathematics underlying computation and compare calculations in real-time using wearable sensors and force platforms with the estimated with the standard method used by ergonomists and occupational health and safety technicians. To reach this aim, 10 participants performed six lifting tasks under two risk conditions. The results show us that the value rapidly converges towards the reference value in all tasks, suggesting a promising use of adaptive algorithms and instrumental tools for biomechanical risk assessment.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10934623 | PMC |
http://dx.doi.org/10.3390/s24051474 | DOI Listing |
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