An Empirical Analysis on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data
Bibliographic record
Abstract
In this data age, connected devices are continuously generating petabytes of images, text, and internet of things (IoT) sensor data.One approach to efficiently store this massive data is to extract the relevant and representative features and store only those features instead of the continuous streaming data.However, it raises a question as to the amount of information content we can retain from the data and if we can reconstruct the pseudo-original data when needed.By facilitating relevant and representative feature extraction, storage and reconstruction of near original pattern, we aim to address some of the challenges faced by the explosion of the streaming data.We present a preliminary study, where we explored multiple autoencoders for the concise feature extraction and reconstruction for human activity recognition (HAR) sensor data.Our Multi-Layer Perceptron (MLP) deep autoencoder achieved a storage reduction of 90.18%, where as convolutional autoencoder achieved 11.18%.For Long-Short Term Memory (LSTM) autoencoder the reduction was 91.47% and for convolutional LSTM autoencoder it was 72.35%.The storage reduction depended on the size and dimension of the concise representation.For higher dimensions of the representation, the storage reduction was low.But relevant information retention was high which was validated by the classification performed on the reconstructed data.
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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 | 0.005 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".