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Updated: Sep 26, 2026
The Measurement of Unsteady Surface Pressure Using a Remote Microphone Probe
Published on: December 3, 2016
A Modular Wireless Pressure Sensing Framework with Surface-Dependent Calibration for Data-Guided Pressure Injury
Objective:
Pressure injury (PI) prevention requires continuous assessment of localized tissue loading, yet conventional repositioning protocols are labor-intensive and do not account for inter-subject or support-surface variability. This study developed and evaluated a modular wireless sensing framework, the Modular Pressure-Responsive Embedded Sensing System (M-PRESS), for data-guided PI monitoring using localized load sensing, surface-dependent calibration, and threshold-guided intervention.
Methods:
M-PRESS is a site-configurable platform designed for anatomically high-risk regions. A surface-dependent calibration method was implemented to compensate for rigid and compliant support conditions. In Phase I, healthy participants across body mass index (BMI) groups were studied to characterize load-skin response relationships and establish BMI-informed alert thresholds. In Phase II, a pilot quasi-experimental study compared M-PRESS-guided monitoring with standard repositioning care in high-risk inpatients.
Results:
Phase I identified distinct BMI-dependent sustained-loading patterns, supporting alert thresholds of 0.7 kgf for underweight participants and 0.9 kgf for overweight participants. In Phase II, no new PIs developed in either group during the 4-h observation period. Compared with standard care, M-PRESS-guided monitoring was associated with a 93% shorter nurse-assisted intervention time (0.63 vs. 9.32 min), and operational variability was significantly lower (Moses test, P < 0.001).
Conclusions:
M-PRESS demonstrated preliminary feasibility as a modular load-based sensing framework for data-guided PI monitoring in high-risk inpatients.
Significance:
By integrating site-specific sensing, surface-dependent calibration, BMI-informed thresholding, and targeted local load relief, the proposed system provides a feasible monitoring-to-intervention framework for data-guided PI monitoring and future patient-specific intervention studies.
