Fatigue assessment in multi-activity manual handling tasks through joint angle monitoring with wearable sensors
Bibliographic record
Abstract
• Using joint motion and coordination data in an FFNN showed reasonable accuracy in detecting performance fatigue. • Performance fatigue resulted in reduced coordination of adjacent joints, assessed by mutual information. • Comparing end-to-end and feature-engineering models showed motion and coordination data’s potential in fatigue detection. • Segmenting manual handling tasks revealed activity-related joint behavior and its importance for fatigue analysis. Performance fatigue is a primary contributor to work-related musculoskeletal disorders and understanding its impact during manual handling tasks (MHT) is crucial to preventing such issues. This study evaluated fatigue during prolonged MHT by analyzing body joint angles kinematics and their coordinative variability using readouts from inertial measurement units (IMUs). Eight individuals participated in the MHT experiment, continuously reporting their fatigue levels. The MHT was further segmented into repetitive activities of lifting, carrying, and lowering, and kinematic metrics (average, maximum joint excursions, variability) were extracted from the body joint angles in the sagittal plane. During lifting and lowering repetitions, mean and peak joint angles increased with fatigue levels across all joints except the knee, where both decreased, with average Spearman’s ρ values of −0.24 and −0.16 during lifting, respectively. Furthermore, as fatigue progressed, coordination among adjacent joints decreased, indicated by reduced information transmission measured by mutual information theory. Particularly, the knee-hip mutual information during carrying activity decreased with fatigue (average correlation coefficient: −0.47). Finally, using the proposed features, a feed-forward neural network model achieved a subject-independent accuracy of 66 % in detecting five stages of perceived fatigue. Comparatively, a multi-head convolutional neural networks and long-short-term memory networks using the normalized raw joint angle data achieved 74 % accuracy while requiring significantly greater data and computational resources. These findings provide insights into how fatigue affects joint kinematics and coordination, enhancing our understanding of fatigue-related risk of work-related musculoskeletal disorders. Further investigations are needed to characterize such risks.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".