Estimating Lumbar Spine Compression Using Markerless Motion Capture
Bibliographic record
Abstract
Compressive forces at the L5-S1 intervertebral joint are a contributing factor to low back pain, a leading cause of work-related musculoskeletal disorders. Estimating these forces in field settings and comparing them to safety limits can support ergonomic risk assessments. While musculoskeletal modeling platforms such as OpenSim provide accurate estimations of spinal loading, their reliance on expert operation and high computational demands limits their practicality in field settings. This study presents a biomechanical model that estimates L5-S1 compression force using joint angles derived from a markerless motion capture system, along with the user’s body weight, sex, and the lifted load. A proof-of-concept evaluation was conducted with one participant performing symmetric lifting tasks at 5, 10, and 15 kg. The proposed method’s outputs were compared to OpenSim estimates, showing close agreement in peak compression force for 10 and 15 kg conditions, with normalized peak estimation errors of 4 ± 2% and 2 ± 1% during lifting and 11 ± 5% and 5 ± 4% during lowering, respectively. Larger errors were observed for the 5 kg condition and during mid-movement for all load conditions, likely due to unmodeled muscle co-contraction and static assumptions. Overall, the method shows promise for accessible in-field ergonomic assessment of peak spinal loads during lifting and provides a foundation for evaluating interventions such as occupational exoskeletons in real-world settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".