Foot Pressure Monitoring By Physiological Parameters And Early Detection Of Pvd
Bibliographic record
Abstract
In today's demanding lifestyles, prolonged standing and sudden movements often lead to fatigue, affecting individuals, especially athletes, who rely on optimal foot performance. Additionally, conditions like Peripheral Vascular Disease (PVD) pose significant health risks, necessitating early detection for effective management. To address these concerns, we propose integrating foot pressure monitoring technology with physiological parameters such as heart rate, blood oxygen saturation (SpO2), and temperature. This holistic approach aims to develop a comprehensive monitoring system capable of early PVD detection. By embedding a GSM Module with a MAX30102 sensor into footwear, real-time foot pressure distribution can be monitored, providing insights into gait abnormalities and potential circulatory issues. Concurrently, monitoring physiological parameters offers additional indicators of vascular health and perfusion. This integrated system offers a proactive approach to foot health monitoring, empowering individuals, particularly athletes, to optimize performance and mitigate health risks associated with conditions like PVD. Early detection through this system can lead to timely interventions, enhancing overall well-being and quality of life.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".