Towards Automatic Feature Extraction for Activity Recognition from Wearable Sensors: A Deep Learning Approach
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a novel approach for activity recognition from accelerometer data. Existing approaches usually extract hand-crafted features that are used as input for classifiers. However, hand-crafted features are data dependent and could not be generalized for different application domains. To overcome these limitations, our approach relies on matrix factorization for dimensionality reduction and deep learning algorithm such as a stacked auto-encoder to automatically learn suitable features, which will be then fed into a softmax classifier for classification. Our approach has potential advantages over existing approaches in terms of automatic feature extraction and generalization across different application domains. The proposed approach is validated using extensive experiments on various publicly available datasets. We empirically demonstrate that our proposed approach accurately discriminates between human activities and performs better than several state-of-the-art approaches.
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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 | 0.001 | 0.001 |
| 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 | 0.003 |
| 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