Deep Neural Networks for Human Activity Recognition With Wearable Sensors: Leave-One-Subject-Out Cross-Validation for Model Selection
Why this work is in the frame
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Bibliographic record
Abstract
Human Activity Recognition (HAR) has been attracting significant research attention because of the increasing availability of environmental and wearable sensors for collecting HAR data. In recent years, deep learning approaches have demonstrated a great success due to their ability to model complex systems. However, these models are often evaluated on the same subjects as those used to train the model; thus, the provided accuracy estimates do not pertain to new subjects. Occasionally, one or a few subjects are selected for the evaluation, but such estimates highly depend on the subjects selected for the evaluation. Consequently, this paper examines how well different machine learning architectures make generalizations based on a new subject(s) by using Leave-One-Subject-Out Cross-Validation (LOSOCV). Changing the subject used for the evaluation in each fold of the cross-validation, LOSOCV provides subject-independent estimate of the performance for new subjects. Six feed forward and convolutional neural network (CNN) architectures as well as four pre-processing scenarios have been considered. Results show that CNN architecture with two convolutions and one-dimensional filter accompanied by a sliding window and vector magnitude, generalizes better than other architectures. For the same CNN, the accuracy improves from 85.1% when evaluated with LOSOCV to 99.85% when evaluated with the traditional 10-fold cross-validation, demonstrating the importance of using LOSOCV for the evaluation.
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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 | 0.001 | 0.004 |
| Open science | 0.001 | 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 it