Wearable sensors in Industry 4.0: Preventing work-related musculoskeletal disorders
Bibliographic record
Abstract
Work-related musculoskeletal disorders (WMSDs) are a global health and economic challenge, particularly in industrialized nations, accounting for up to 2 % of GDP losses due to disability and productivity reduction. Wearable sensors, driven by Industry 4.0 advancements, offer transformative potential for real-time ergonomic assessment and injury prevention. This systematic review analyzes 40 peer-reviewed studies (2013–2024) to evaluate the application of inertial measurement units (IMUs), electromyography (EMG) sensors, and pressure sensors in mitigating WMSD risks. Findings demonstrate that wearable technologies enhance workplace safety through real-time feedback, reducing ergonomic risks and improving productivity. Despite promising advancements, challenges such as scalability, user comfort, and data privacy persist. This review emphasizes the need for standardized protocols, ethical frameworks, and deeper integration with machine learning to optimize sensor accuracy and usability. Future research directions include advancing AI-driven predictive ergonomics, addressing privacy concerns, and improving sensor design for widespread industrial adoption. This study provides actionable insights to bridge the gap between academic research and practical deployment in diverse industrial settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".