A novel approach to provide safe indoor industrial environment
Bibliographic record
Abstract
In this paper, a new method to have a safe place by human motion recognition and routing in an indoor environment, in presence of different objects, such as on a manufacturing shop floor, is presented. Due to the high cost and limitations of some of the available methods such as video surveillance, limitations in the indoor because of the presence of other objects, processing time, and limitation of data storage, a low cost, low power, and high accuracy approach using Wireless Sensor Network (WSN) is proposed. In this proposed method we collect data from indoor area using nodes equipped by sensors, and use them for processing. Features are extracted from signals which are sensed by sensors and applied for motion recognition. Subsequently, they are processed to recognize motion by Hidden Markov Model (HMM) classification. HMM are tested along with an investigation of their accuracy. Rules are extracted by the routing and motions. The presented method is evaluated for three distinct motion categories that are common in indoor environment work places. Experimental results show the high accuracy is obtained for the proposed approach for the case under study which was at 100%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".