Multichannel ECG recording from waist using textile sensors
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
BACKGROUND: The development of wearable health monitoring systems is garnering tremendous interest in research, technology and commercial applications. Their ability of providing unique capabilities in continuous, real-time, and non-invasive tracking of the physiological markers of users can provide insights into the performance and health of individuals. Electrocardiogram (ECG) signals are of particular interest, as cardiovascular disease is the leading cause of death globally. Monitoring heart health and its conditions such as ventricular disturbances and arrhythmias can be achieved through evaluating various features of ECG such as R-peaks, QRS complex, T-wave, and P-wave. Despite recent advances in biosensors for wearable applications, most of the currently available solutions rely solely on a single system attached to the body, limiting the ability to obtain reliable and multi-location biosignals. However, in engineering systems, sensor fusion, which is the optimal integration and processing of data from multiple sensors, has been a common theme and should be considered for wearables. In recent years, due to an increase in the availability and variety of different types of sensors, the possibility of achieving sensor fusion in wearable systems has become more attainable. Sensor fusion in multi-sensing systems results in significant enhancements of information inferences compared to those from systems with a sole sensor. One step towards the development of sensor fusion for wearable health monitoring systems is the accessibility to multiple reliable electrophysiological signals, which can be recorded continuously. RESULTS: In this paper, we develop a textile-based multichannel ECG band that has the ability to measure ECG from multiple locations on the waist. As a proof of concept, we demonstrate that ECG signals can be reliably obtained from different locations on the waist where the shape of the QRS complex is nearly comparable with recordings from the chest using traditional gel electrodes. In addition, we develop a probabilistic approach-based on prediction and update strategies-to detect R-peaks from noisy textile data in different statuses, including sitting, standing, and jogging. In this approach, an optimal search method is utilized to detect R-peaks based on the history of the intervals between previously detected R-peaks. We show that the performance of our probabilistic approach in R-peak detection is significantly better than that based on Pan-Tompkins and optimal-threshold methods. CONCLUSION: A textile-based multichannel ECG band was developed to track the heart rate changes from multiple locations on the waist. We demonstrated that (i) the ECG signal can be detected from different locations on the waist, and (ii) the accuracy of the detected R-peaks from textile sensors was improved by using our proposed probabilistic approach. Despite the limitations of the textile sensors that might compromise the quality of ECG signals, we anticipate that the textile-based multichannel ECG band can be considered as an effective wearable system to facilitate the development of sensor fusion methodology for pervasive and non-invasive health monitoring through continuous tracking of heart rate variability (HRV) from the waist. In addition, from the commercialization point of view, we anticipate that the developed band has the potential to be integrated into garments such as underwear, bras or pants so that individuals can use it on a daily basis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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