Binary Classifiers for Data Integrity Detection in Wearable IoT Edge Devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a comparison of several artificial intelligence (AI) based binary classifiers for detecting the integrity of data obtained from Internet of Things (IoT) enabled wearable sensors. Detecting the integrity of data at the network edge facilitates the elimination of corrupted or unusable data, which translates to a lower amount of data stored and transmitted. This reduces the storage and power requirements of IoT devices without a reduction in functionality. In this work, we explore several machine learning-based classifiers to check the integrity of electrocardiogram (ECG) data. The feature vectors are derived from low complexity kurtosis and skewness based Signal Quality Indices (SQIs). From the experiments, it is found that a bagged ensemble of 3 neural networks achieves the highest detection accuracy of 99.47%. We also estimated the complexity and power consumed by the various classifier implementations and classifier fusion implementations. The energy consumed by the ensemble classifier was estimated to be around 0.039 nJ.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 it