Action Recognition Using a Wristband-Type Activity Tracker: Case Study of Masonry Work
Bibliographic record
Abstract
Given that labor is one of the most important resources in a construction project, collecting field data on workers’ activities (i.e., work sampling) is critical to understanding and managing workers’ performance for a productivity analysis. Unlike manual observation used for work sampling, automated action recognition and analysis using sensors, such as motion and image sensors, enable continuous worker monitoring and corresponding task assessment. Among diverse sensors, an accelerometer has great potential for automated action recognition due to its data richness and mobility. In this paper, we propose wrist-worn accelerometer-based action recognition with selected features and classifiers and apply it to masonry work to demonstrate its feasibility. The novelty of this approach is the use of a single affordable wrist-worn sensor, which would not interfere with workers’ ongoing work. The result shows that Multilayer Perceptron classifier can achieve about 97% of accuracy in posture classification in masonry work. The proposed approach has an immense potential to be used for non-intrusive action recognition for construction workers, which can open a door for diverse productivity analyses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".