Logistic Model Tree for Human Activity Recognition Using Smartphone-Based Inertial Sensors
Bibliographic record
Abstract
Human Activity Recognition (HAR) systems using sensor data have widespread use in many real-life applications, making it an important emerging area of research. As inertial sensors are readily available in many handheld devices, HAR systems are generally designed based on the data obtained from them. In this paper, the Logistic Model Trees (LMT) machine learning method for predicting the human motion from smartphone-based inertial sensors is considered. This study aims to demonstrate the capabilities of LMT in obtaining higher prediction rates even with short time segment of data (1 sec), in comparison with longer time segments (2.5 sec) used in the literature. The performance of HAR system designed with LMT is compared with those designed with Random Forest (RF) and Logistic Regression Tree (LR) for a set of dynamic and static activities. The system is trained and tested on two publically available datasets, namely WISDM and UCI HAR. The proposed LMT method outperforms RF and LR by achieving recognition accuracies 90.86% and 94.02% on WISDM and UCI HAR respectively, and achieves between 89.82% - 88.73% overall accuracy during cross-dataset evaluation.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".