University of Twente Student Theses
Marker-Less Prediction of L5/S1 Compression Loads during Dynamic lifting Activities using a musculoskeletal approach
Rubiano Blanco, Daniela Sofia (2024) Marker-Less Prediction of L5/S1 Compression Loads during Dynamic lifting Activities using a musculoskeletal approach.
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| Abstract: | Lower back disorders are a significant health concern for industrial workers. Assessing compressive spine loads is essential for predicting low back pain (LBP). Marker-based motion tracking systems are the gold standard for human motion analysis to understand the biomechanics of the spine and its relation to LBP. However, markers placement is labor-intensive and intrusive. Existing marker-less methods often require multiple cameras and do not consider muscle forces, which are crucial for precise movement analysis. To address these limitations, this study designs and tests a marker-less approach using a single Microsoft Kinect, Nuitrack and a musculoskeletal model to compute L5/S1 joint compression loads during dynamic lifting activities. Ten participants performed different static and dynamic lifting techniques while lifting a 5kg dumbbell. OpenSim was used for inverse kinematics, dynamics, and load computations, while CEINMS handled muscle forces. The marker-less method showed strong agreement with the marker-based system, with R² values between 0.84 to 0.93 and RMSE ranging from 0.33 to 0.68 for inverse kinematics results. ANOVA results indicated significant differences between peak compression load estimates between the marker-less and marker-based methods (p$<$0.05). This research validates the marker-less approach for accurately computing L5/S1 forces, highlighting the significance of muscle force analysis and demonstrating its potential to be used in industrial environments, thereby improving safety. |
| Item Type: | Essay (Master) |
| Faculty: | ET: Engineering Technology |
| Programme: | Biomedical Engineering MSc (66226) |
| Link to this item: | https://purl.utwente.nl/essays/99937 |
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