Hierarchical Classifier for Improved Human Activity Recognition using Wearable Sensors
Bibliographic record
Abstract
Hierarchical classification (HC) is a multiclass classification approach that is effective in solving complex classification problems. However, HC in Human Activity Recognition (HAR) is still relatively unexplored. This paper investigates the effectiveness of HC compared to flat classification (FC) and ensemble-based approaches for classifying complex human activities based on inertial measurement unit (IMU)-based data. To conduct the comparative analysis, support vector machine (SVM) and decision tree (DT) methods are considered as the base models. and a comparison between various approaches, including traditional flat SVM, DT, SVM-based Bagging, random forest (RF), SVM-based AdaBoost, DT-based AdaBoost, SVM-based hierarchical classification (HC), and DT-based HC is carried out. Experiments conducted on two publicly available datasets, namely, mHealth and DaLiAc, indicate that HC is able to achieve better classification results than ensemble methods and is able to achieve them utilizing fewer base models.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".