Smart T-Shirt Based on Wireless Communication Spiral Fiber Sensor Array for Real-Time Breath Monitoring: Validation of the Technology
Bibliographic record
Abstract
In this paper, we present the design and the validation of a new smart textile developed for real time monitoring of human breath. The smart textile is a wearable stretching T-shirt featuring an array of six contactless and non-invasive sensors placed on human's thoraco-abdominal walls. The wireless sensors communicate quasi-simultaneously the data to a detection base station through Bluetooth protocol. The sensor is composed of a spiral shaped multi-material fiber antenna with a central frequency at 2.4 GHz connected to a compact Bluetooth transmitter. The sensors are integrated on the stretchable T-shirt without compromising the user's comfort. The sensing mechanism of the system is based on the shift of the central frequency of the spiral fiber antenna induced by the deformation of the thorax and the abdomen during the breath. As a consequence, the breathing pattern is obtained through the received signal strength indicator recorded by a portable detection base station. With the sensor array integrated into a T-shirt, we demonstrate the feasibility assessment for monitoring breathing of seven volunteers in seating and standing positions. Using an array of sensors strategically placed on the T-shirt provides a more reliable approach to detect interpretable breathing patterns by sensing the movement of the abdomen and the thorax at the same time. Using a commercial spirometer as a standard reference, we assessed the validity of breathing parameters (ie; expiration and inspiration times, breathing period, and breathing rate) measured with the T-shirt. Based on Bland-Altman statistical analysis, a good agreement between textile-based sensors and the standard reference was obtained. We were able to show that the smart T-shirt could detect breathing patterns and pauses in breathing, which could be very useful to monitor sleep apnea and clinical monitoring of patients.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".