The surge in chronic diseases and an aging global demographic are intensifying demand for remote patient monitoring and personalized healthcare solutions. Simultaneously, a growing focus on evidence-based medicine and reducing healthcare costs drives the need for objective diagnostic and management tools. This technology aligns perfectly, offering a scalable, non-invasive method to standardize care, enhance patient self-management, and integrate seamlessly into emerging digital health ecosystems worldwide.
Significantly Reduces Patient Burden: Utilizes a small, finger-worn device for non-invasive, simple compression pressure measurement, significantly reducing patient discomfort and stress compared to conventional, cumbersome methods.
Enables Quantitative Compression Pressure Assessment: Assesses compression pressure with objective numerical data, eliminating reliance on expert intuition. This contributes to treatment standardization, reduces medical errors, and enables high-quality healthcare delivery.
Designed for Easy Field Implementation: Combines a flexible, elastic device body with a strain sensor, allowing flexible adaptation to various elastic garments and affected areas. It can be implemented without significant capital investment.
This patent protects a compression pressure estimation system featuring a finger-worn measurement device with a flexible, elastic body and a strain sensor. The claims are well-defined and robust, having been granted without office actions despite existing prior art, indicating strong novelty and inventive step.
This patent primarily covers the finger-worn device and its pressure estimation algorithm. White space exists in developing AI-driven predictive analytics for patient outcomes or integrating this technology into smart textiles for dynamic compression adjustment.
Assuming a conventional expert compression pressure evaluation costs ~$35 (AI est.) per session. This technology could reduce evaluation time and improve accuracy, potentially decreasing re-examination rates by 20%. For a facility conducting 10,000 evaluations annually, this could save ~$65K (AI est.) in re-examination costs. Furthermore, optimizing specialist labor could yield an additional ~$100K (AI est.) in annual savings.
X: Measurement Accuracy & Reproducibility
Y: Implementation Cost & Operational Burden