Modeling and Reconstructing Textile Sensor Noise: Implications for Wearable Technology
Bibliographic record
Abstract
Wearable sensors enable the simultaneous recording of several electrophysiological signals from the human body in a non-invasive and continuous manner. Textile sensors are garnering substantial interest in the wearable technology because they can be knitted directly into the daily-used objects like underwear, bra, dress, etc. However, accurate processing of signals recorded by textile sensors is extremely challenging due to the very low signal-to-noise ratio (SNR). Systematic classification of textile sensor noise (TSN) is necessary to: (i) identify different types of noise and their statistical characteristics, (ii) explore how each type of noise influences the electrophysiological signal, (iii) develop optimal textile-specific electronics that suppress TSN, and (iv) reproduce TSN and create large dataset of textile sensors to validate various machine learning and signal processing algorithms. In this paper, we develop a novel technique to classify textile sensor artifacts in ECG signals. By simultaneously recording signals from the waist (textile sensors) and chest (gel electrode), we extract TSN by removing the chest ECG signal from the recorded textile data. We classify TSN based on its morphological and statistical features in two main categories, namely, slow and fast. Linear prediction coding (LPC) is utilized to model each class of TSN by auto-regression coefficients and residues. The residual signal can be approximated by Gaussian distribution which enables reproducing slow and fast artifacts that not only preserve the similar morphological features but also fulfill the statistical properties of TSN. By reproducing TSN and adding them to clean ECG signals, we create a textile-like ECG signal which can be used to develop and validate different signal processing algorithms.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".