ADVANCED VERSION OF IEEE802.15.6 BASED ON COMPRESSED SENSING FOR INTELLIGENT BODY SENSOR NETWORKS
Bibliographic record
Abstract
Intelligent Body Sensor Networks (IBSNs) based on IEEE 802.15.6, have emerged as a key technology to provide wearable wireless health care systems to establish real-time health monitoring algorithms for long-term recording of the vital signals and diagnose many life-threatening diseases. IBSNs allow continuous monitoring of patients at anytime and anywhere in the hospital and help in prevention medicine and early detection of pathologies to improve the quality of life. The IBSN system is also used in different types of wireless healthcare systems, including monitoring patients with chronic disease, patients in hospitals, elderly patients at home, or even real-time monitoring of patients in any kind of environment. In particular, it can be used for establishing a wearable and wireless health care system. The current version of IEEE 802.15.6 is based on conventional sampling approaches which suffers from many samples and power consumption. In addition, it is not enough to support high data rates applications for advanced wearable and wireless healthcare systems. The main purpose of this paper is to present an advanced version of IEEE 802.15.6 based on Compressed Sensing (CS) and focuses the most important features for wearable and wireless healthcare systems to support low data rate, low power consumption wireless communication in, on or around the body. The advanced version of IEEE 802.15 standard based on a CS focus on low complexity, low cost and ultra-low-power consumption infrastructures using a random sensing matrix to establish a wireless communication standard optimized for ultra-low-power in-body/ on-body wireless nodes to serve a variety of wearable and wireless medical applications. The proposed algorithm out-performs existing algorithm by achieving a good level of Quality of Service (QoS) and Signal-to Noise Ratio (S/N) at the receiver of each wireless node. This ability allows reducing 12 % of Average Packet Delay (APD) at the receiver of each wireless node. The proposed architecture is increased 10 % of throughput and 8 % of S/N at the receiver of each wireless node, which provide a good background for establishing high qualified Wireless Body Area Networks (WBANs).
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".