Activity and environment classification using foot mounted navigation sensors
Bibliographic record
Abstract
Foot mounted navigation systems can be deployed for tracking military personnel, first responders and offenders. Determining the activity and environment of individuals can provide valuable information to those monitoring these individuals. This paper provides activity and environment classification for a foot mounted device that uses an IMU and GPS receiver. Using information from the navigation filter (e.g. velocity), GPS signal tracking parameters, and IMU measurements this paper presents an algorithm that classifies the following activities: indoor, outdoor, stationary, crawling, walking, running, biking, moving in vehicle, level, up or down elevator and up or down stairs. Multiple probability density functions that map each feature (i.e. metric) to an activity are provided. Then a naive Bayesian probabilistic model is used to determine the probability of an activity. To improve reliability and accuracy of the classification several conditions are added. The algorithm shows excellent results for activity classification, however environment classification is less reliable due to variations in GPS tracking abilities as a function of the environment. Results are shown with images from the data collection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".