On Recognizing Abnormal Human Behaviours by Data Stream Mining with Misclassified Recalls
Bibliographic record
Abstract
Human activity recognition (HAR) has been a popular research topic, because of its importance in security and healthcare contributing to aging societies. One of the emerging applications of HAR is to monitor needy people such as elders, patients of disabled, or undergoing physical rehabilitation, using sensing technology. In this paper, an improved version of Very Fast Decision Tree (VFDT) is proposed which makes use of misclassified results for post-learning. Specifically, a new technique namely Misclassified Recall (MR) which is a post-processing step for relearning a new concept, is formulated. In HAR, most misclassified instances are those belonging to ambiguous movements. For examples, squatting involves actions in between standing and sitting, falling straight down is a sequence of standing, possibly body tiling or curling, bending legs, squatting and crashing down on the floor; and there may be totally new (unseen) actions beyond the training instances when it comes to classifying "abnormal" human behaviours. Think about the extreme postures of how a person collapses and free falling from height. Experiments using wearable sensing data for multi-class HAR is used, to test the efficacy of the new methodology VFDT+MR, in comparison to a classical data stream mining algorithm VFDT alone.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".