An Evaluation Method of Assessing the Low Back Muscle Fatigue in Manual Material Handling
Bibliographic record
Abstract
Workers in certain industries are exposed to high labor-intensive tasks. Low back pain (LBP) is widespread among construction workers (Hildebrandt, 1995) and is extremely prevalent, with every adult having up to an 85% chance of experiencing LBP at least once during his or her lifetime. Back-related complaints are more costly than those from any other body part for Alberta work-related claims. Ergonomic principles in addition to the engineering considerations should be included in the design of work stations to minimize the risk of injury for employees. In this study, we assessed the low back muscle fatigue due to lifting tasks. The objective was to investigate the changes in muscle activity and kinematics of the human body caused by fatigue due to repetitive lifting tasks. Three healthy male volunteers with no recent back complications were asked to complete 3 cycles of lifting task at the Syncrude Centre of the Glenrose Rehabilitation Hospital. Each cycle involved lifting a 15lb window frame for 20 times. Self? adhesive reflective markers were attached on the hands, trunk, and legs to measure displacements and rotation of the body parts while performing the task. Electromyography (EMG) sensors were placed on lower back muscles to record their activity. The muscle fatigue was investigated by studying change in EMG spectral parameters such as RMS, mean frequency (MNF), and median frequency (MDF) as well as physical condition of subjects due to repetitive lifting. The power frequency curve shifted to the low frequency when muscle fatigue occurred. As a result, the slope of the RMS, and MNF indicators were successful to describe the fatigue behavior expected.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".