A New Ontology-Based Approach for Human Activity Recognition from GPS Data
Bibliographic record
Abstract
Mobile technologies have deployed a variety of Internet–based services via location based services. The adoption of these services by users has led to mammoth amounts of trajectory data. To use these services effectively, analysis of these kinds of data across different application domains is required in order to identify the activities that users might need to do in different places. Researchers from different communities have developed models and techniques to extract activity types from such data, but they mainly have focused on the geometric properties of trajectories and do not consider the semantic aspect of moving objects. This work proposes a new ontology-based approach so as to recognize human activity from GPS data for understanding and interpreting mobility data. The performance of the approach was tested and evaluated using a dataset, which was acquired by a user over a year within the urban area in the City of Calgary in 2010. It was observed that the accuracy of the results was related to the availability of the points of interest around the places that the user had stopped. Moreover, an evaluation experiment was done, which revealed the effectiveness of the proposed method with an improvement of 50 % performance with complexity trend of an O(n).
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".