Activity recognition through goal-based segmentation
Bibliographic record
Abstract
A major issue in activity recognition in a sensor network is how to automatically segment the low-level signal sequences in order to optimize the probabilistic recognition models for goals and activities. Past efforts have relied on segmenting the signal sequences by hand, which is both time-consuming and error-prone. In our view, segments should correspond to atomic human activities that enable a goal-recognizer to operate optimally; the two are intimately related. In this paper, we present a novel method for building probabilistic activity models at the same time as we segment signal sequences into motion patterns. We model each motion pattern as a linear dynamic model and the transitions between motion patterns as a Markov process conditioned on goals. Our EM learning algorithm simultaneously learns the motion-pattern boundaries and probabilistic models for goals and activities, which in turn can be used to accurately recognize activities in an online phase. A major advantage of our algorithm is that it can reduce the human effort in segmenting and labeling signal sequences. We demonstrate the effectiveness of our algorithm using the data collected in a real wireless environment. Copyright © 2005. American Association for Artificial Intelligence (www.aaai.org). All rights reserved.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".