Andrew T. Gothard

In orthopedics, the evaluation of footbed pressure distribution maps is a valuable gait analysis technique that aids physicians in diagnosing musculoskeletal and gait disorders. Recently, the use of pressure-sensing insoles to collect pressure distributions has become more popular due to the passive collection of natural gait data during daily activities and the reduction in physical strain experienced by patients. However, current pressure-sensing insoles face the limitations of low customizability and high cost. Previous works have shown the ability to construct customizable pressure-sensing insoles with capacitive sensors using fused-deposition modeling (FDM) 3D printing. This work explores the feasibility of low-cost fully and continuously 3D printed pressure sensors for pressure-sensing insoles using three sensor designs, which use flexible thermoplastic polyurethane (TPU) as the dielectric layer and either conductive TPU or conductive polylactic acid (PLA) for the conductive plates. The sensors are paired with a commercial capacitance-to-voltage converter board to form the sensing system. Dynamic sensor performance is evaluated via sinusoidal compressive tests at frequencies of 1, 3, 5, and 7 Hz, with pressure levels varying from 14.33 to 23.88, 33.43, 52.54, and 71.65 N/cm² at each frequency. Five sensors of each type are tested. Results show that all sensors display significant hysteresis and nonlinearity. The PLA-TPU sensor with 10% infill is the best-performing sensor with the highest average sensitivity and lowest average hysteresis and linearity errors. The range of average sensitivities, hysteresis, and linearity errors across the entire span of tested pressures and frequencies for the PLA-TPU sensor with 10% infill is 11.61–20.11·10⁻⁴ V/(N/cm²), 11.9–31.8%, and 9.0–22.3%, respectively. The significant hysteresis and linearity error are due to the viscoelastic properties of TPU, and some additional nonlinear effects may be due to buckling of the infill walls of the dielectric.

In this study, a new algorithm is proposed to improve impact event localization for vibration-based smart building applications. Vibration-based smart building systems, which use floor-mounted accelerometers, have the potential to be a less intrusive way to monitor building occupants. Vibration-based localization algorithms typically use a time or energy-based approach or a combination of the two. Time-based approaches rely on time-difference-of-arrival (TDOA) calculations and can have large errors due to the variability of the wave velocity through the floor. The variability in wave velocity is caused by the dispersive properties of concrete, wave reflections from walls, and discontinuities in the floor structure such as beams under the floor. Energy-based localization methods have been shown to overcome the problem of variable wave velocity by using an exponential decay model of the generated wave energy to predict the location of an impact. Previous energy-based methods have been shown to give sub-meter accuracy for measurement systems with accelerometers mounted on the surface of the floor as well as systems with accelerometers mounted under the floor. However, available energy-based methods are limited to exponential decay models, which are only applicable to homogeneous floor structures. Supporting floor structures, such as beams, and walls create inhomogeneities, which cause the wave to decay differently in each direction and require a more complex model to describe. This work proposes a new velocity and energy ratio mapping (VERM) localization algorithm to overcome this limitation. The VERM algorithm computes ratios of response energy between pairs of sensors for impact locations across the floor structure to be monitored and uses the resulting energy ratio maps to find potential impact locations. The VERM algorithm predicts a final impact location using velocity maps to compare estimated TDOA values to the TDOA value of the measured signal. Experimental validation of the method is performed by collecting hammer impact data from 70 locations in a classroom within the Lab Science Commons Building at Tennessee Technological University from three under-floor-mounted sensors and creating the energy ratio maps. Results of this study show that the VERM algorithm has an accuracy, average distance error, and maximum distance error of 77.14%, 1.89 ft, and 12.34 ft, respectively. Additionally, the results show that the algorithm produces a set of potential locations where at least one potential location is correct 90% of the time showing the algorithm can be improved to have higher accuracy.