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Author ORCID Identifier


Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program

Mechanical Engineering

Year Degree Awarded


Month Degree Awarded


First Advisor

Frank C. Sup IV

Subject Categories

Manufacturing | Other Mechanical Engineering


Pressure Injuries (PIs), commonly known as pressure ulcers or bedsores, affect about 2.5 million individuals yearly in the United States [1]. Pressure, shear, and micro-climate are the top external factors concerning PI formation [2]. Most commercial interface pressure sensing systems have several limitations, including cost [3], lack of micro-climate and shear measurements, and possible inaccuracies due to calibration [4–8]. These limitations signal the need for a new generation of sensors. The two aims of this dissertation are to determine the feasibility of magnetic sensing in the context of PI research and to probe the possibility of predicting pressures and displacements at the interface using an analytical model. The first objective entailed manufacturing, calibrating, and characterizing a soft magnetic sensor via % Full-Scale Output (% FSO) errors. Then, the soft sensor’s performance was compared with a commercial alternative via a protocol inspired by the ANSI/RESNA Support Surfaces Standard (RESNA SS-1:2019). These comparisons were quantified using 4 experimental scenarios via bootstrapped confidence intervals. The second objective involved the calculation of surface pressures and displacements based on contact mechanics equations and comparing them to two experimental scenarios. This work produced three characterizations, four comparisons to evaluate the soft sensor performance, and two calculations to evaluate the feasibility of analytical predictions. The soft sensor characterization involved two incline conditions (0° and 30°) with either random or sequential loading. The 0° incline at random loading yielded a 7 %FSO and 1 %FSO on average for compression and shear, respectively. At a 0° incline with sequential loading, the average results were 3 %FSO and 1 %FSO for compression and shear with 2 % hysteresis in compression. The 30° incline at random loading yielded 1 %FSO and 2 %FSO for compression and shear on average. The four comparisons were quantified via the bootstrapped difference of means with 95% confidence intervals. For the flat punch at 0 degrees in compression, the difference of means estimate was 2.1 mmHg (1.7 mmHg, 2.6 mmHg) and shear 0.7 mmHg (0.5 mmHg, 1.0 mmHg). For the STDI at 0 degrees in compression, 1.0 mmHg (-0.1 mmHg, 2.2 mmHg) and shear 0.2 mmHg (0.0 mmHg, 0.4 mmHg). For the STDI at 30 degrees in compression, 10.4 mmHg (9.5 mmHg, 11.2 mmHg) and shear 0.0 mmHg (-0.3 mmHg, 0.2 mmHg). In the last comparison, the flat punch at 30 degrees, in compression, the values are -1.9 mmHg (-2.8 mmHg, -1.2 mmHg), and shear 6.2 mmHg (5.4 mmHg, 6.9 mmHg). Lastly, the analytical solution produced for average interface pressure in flat punch prediction with a relative error of approximately 4%; however, the displacement prediction for the STDI case produced a relative error above 60% for the STDI best case. Thus, only the flat punch prediction might be suitable. The results show potential for this soft sensing modality to be implemented in PI applications, although future works are needed in calibration and testing for generalization and robustness. In addition, future work is needed to estimate foam material parameters such as the elastic modulus and Poisson’s ratio with greater accuracy.


Available for download on Saturday, February 01, 2025