A position-independent method for soil types recognition using inertial data from a wearable device
Bibliographic record
Abstract
This paper describes a novel method for recognizing different soil types based on inertial data generated by a user's gait. To achieve this objective, a new wearable device which aims at collecting data produced by an embedded 6-axis accelerometer/gyroscope was designed first. To command this piece of hardware (start and stop recording, as well as annotate raw data), a mobile application was specifically developed. A total of 70 well-known features both from time and frequency domains that are mostly used in activity recognition were computed over each signal to produce enough discriminating characteristics. Then, two machine learning algorithms (Random Forest and k-Nearest Neighbors) were employed to classify such data. The proposed method was tested with 9 participants on four soil types with an experimental setup close to real use case situations. Results obtained let us state that a soil-types recognition is not only possible but also accurate and reliable since overall median F-Score measures of 82% and 86% were obtained respectively with the Random Forest and the k-NN classifiers. Although the user independence of our system was not proven due to a limited number of involved users, the independence condition of the position of the wearable device was clearly demonstrated.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".