In this work a review of the technology surrounding high deflection strain sensing with an emphasis on that of a recently developed nickel nano-composite strain sensor is presented. A new base silicone material was identified for the nickel nano-composite strain sensor that improves its mechanical stiffness and conductive properties. A previously identified cyclic creep concern was mitigated through preconditioning and the use of adhered backing materials. Through a block design experiment the strain/resistance curves for the strain sensors were characterized over a wide range of nano-filler material compositions. An analytical model was developed based on observation that the resistance of the sensors follows a log-normal response with respect to applied strain. The model demonstrated high fidelity in representing the resistance-strain relationship of the sensors yielding an average R2 value of .93. A standard least squares statistical analysis confirmed strong relationships between curve fit parameters of the modified log-normal model and additive volume fractions with significance at the .05 level for each case. A suitable strain gauge composition was selected for a specific application: a fetal monitoring device. A prototype belt was developed that is worn over the abdomen to detect deflections cause by labor contractions and other fetal movements. Simulation testing on the device was performed and the device was found to be a feasible option for fetal monitoring.

Biological tissues routinely experience large strains and undergo large deformations during normal physiologic activity. Biological tissue deformation is well beyond the range of standard strain gauges, and hence must often be captured using expensive and non-portable options such as optical marker tracking methods that may rely upon significant post-processing. This study develops portable gauges that operate in real time and are compatible with the large strains seen by biological materials. The new gauges are based on a relatively new technique for quantifying large strain in real-time (up to 40 %) by use of a piezoresistive nano-composite strain gauge. The nano-composite strain gauges (NCSGs) are manufactured by suspending nickel nanostrands within a biocompatible silicone matrix. The conductive nickel filaments come into progressively stronger electrical contact with each other as the NCSG is strained, thus reducing the electrical resistance that is then measured using a four-probe method. This thesis summarizes progress in the understanding, design and application of NCSGs for biomechanical applications. The advanced understanding arises from a nano-junction-level finite element analysis of gap evolution that models how the geometry varies with strain in the critical regions between nickel particles. Future work will incorporate this new analysis into global models of the overall piezoresistive phenomenon. The improvements in design focused on the manufacturing route to obtain a reliable thin and flexible gauge, along with a modified connection and data extraction system to reduce drift issues that were present in all previous tests. Furthermore, a pottable data logging system was developed for mobile applications. Finally, a method of analyzing the resultant data was formulated, based upon cross-correlation techniques, in order to distinguish between characteristic wave-forms for distinct physical activities. All of these improvements were successfully demonstrated via a gait-tracking system applied to the insole of standard running shoes.

Flexible high-deflection strain gauges have been demonstrated as cost-effective and accessible sensors for capturing human biomechanical deformations. However, the interpretation of these sensors is notably more complex compared to conventional strain gauges, partially owing to the viscoelastic nature of the strain gauges. On top of the non-linear viscoelastic behavior, dynamic resistance response is even more difficult to capture due to spikes in resistance during strain changes. This research examines the relationships between stress, strain, and resistance in nanocomposite sensors during dynamic strain situations. Under the assumption that both macroscopic stress and resistance are governed by microscopic stress concentrations at the junctions between nanoparticles and silicone matrix, the stress-resistance relationship is analyzed. Both stress and resistance are found to exhibit aspects of viscoelastic behavior, including creep decay and relaxation during constant strains. However, the resistance spikes are found to be more complex than a simple stress-resistance model can capture. This research then develops a model that captures the strain-resistance relationship of the sensors, including resistance spikes, during cyclical movements. The forward model, which converts strain to resistance, is comprised of four parts to accurately capture the different aspects of the sensor response: a quasi-static linear model, a spike magnitude model, a long-term creep decay model, and a short-term decay model. An inverse problem approach is used to create an inverse model, which predicts the strain vs time data that would result in the observed resistance data. The model is calibrated for a particular sensor from a small amount of cyclic data from a single test. The resulting sensor-specific model is able to accurately predict the resistance output with an R-squared value of 0.90. The inverse model is able to accurately predict key strain characteristics with a percent error of 0.5. The model can be used in a wide range of applications, including biomechanical modeling and analysis. It is found that the resistance spikes are directly correlated to the strain acceleration in terms of timing and in terms of magnitude. Poisson contraction rates and voids in the material are possible causes for resistance spikes during dynamic strain movements.

Human knee kinematics, especially during gait, are an important analysis tool. The current "gold standard" for kinematics measurement is a multi-camera, marker-based motion capture system with 3D position tracking. These systems are accurate but expensive and their use is limited to a confined laboratory environment. High deflection strain gauges (HDSG) are a novel class of sensors that have the potential to measure kinematics and can be inexpensive, low profile, and are not limited to measurements within a calibrated volume. However, many HDSG sensors can have a non-linear and non-monotonic response. This thesis explores using a nanocomposite HDSG sensor system for measuring knee kinematics in walking gait and overcoming the non-monotonic sensor response found in HDSGs through advanced modeling techniques. Nanocomposite HDSG sensors were placed across the knee joint in nine subjects during walking gait at three speeds and three inclines. The piezoresistive response of the sensors was obtained by including the sensors in a simple electrical circuit and recorded using a low-cost microcontroller. The voltage response from the system was used in four models. The first two models included a physics-based log-normal model and statistical functional data analysis model that estimated continuous knee angles. The third model was a discrete linear regression model that estimated the inflection points on the knee flexion/extension cycle. Finally, a machine learning approach helped to predict subject speed and incline of the walking surface. The models showed the sensor has the capability to provide knee kinematic data to a degree of accuracy comparable to similar kinematic sensors. The log-normal model had a 0.45 r-squared and was unsuitable as a stand-alone continuous angle predictor. After running a 10-fold cross validation the functional data analysis (FDA) model had an overall RMSE of 3.4° and could be used to predict the entire knee flexion/extension angle cycle. The discrete linear regression model predicted the inflection points on the knee kinematics graph during each gait cycle with an average RMSE of 1.92° for angle measures and 0.0332 seconds for time measures. In every estimate, the discrete linear regression model performed better than the FDA model at those points. The 10-fold cross validation of the machine learning approach using the discrete voltages could predict the categorical incline 90% of the time and the RMSE for the speed model was 0.23 MPH. The use of a HDSG as a knee kinematics sensor was shown as a viable alternative to existing motion capture technology. In future work, it is recommended that a calibration method be developed that would allow this sensor to be used independent of a motion capture system. With these advancements, this inexpensive and low profile HDSG will advance understanding of human gait and kinematics in a more affordable and scope enhancing way.

Chronic low back pain (CLBP) is a nonspecific and persistent ailment that entails many physiological, psychological, social, and economic consequences for individuals and societies. Although there is a plethora of treatments available to treat CLBP, each treatment has varying efficacy for different patients, and it is currently unknown how to best link patients to their ideal treatment. However, it is known that biopsychosocial influences associated with CLBP affect the way that we move. It has been hypothesized that identifying phenotypes of spinal motion could facilitate an objective and repeatable method of determining the optimal treatment for each patient. The objective of this research was to develop an array of high deflection strain gauges to monitor spinal motion, and use that information to identify spinal-motion phenotypes. The high deflection strain gauges used in this endeavor exhibit highly nonlinear electrical signal due to their viscoelastic material properties. Two sub-models were developed to account for these nonlinearities: the first characterizes the relationship between quasistatic strain and resistance, and the second accounts for transient electrical phenomena due to the viscoelastic response to dynamic loads. These sub-models are superimposed to predict and interpret the electrical signal under a wide range of applications. The combined model accurately predicts sensor strain with a mean absolute error (MAE) of 1.4% strain and strain rate with an MAE of 0.036 mm/s. Additionally, a multilayered architecture was developed for the strain gauges to provide mechanical support during high strain, cyclic loads. The architecture significantly mitigates sensor creep and viscoplastic deformation, thereby reducing electrical signal drift by 74%. This research also evaluates the effects of CLBP on patient-reported outcomes. An exploratory factor analysis revealed that there are five primary components of well-being: Pain and Physical Limitations, Psychological Distress, Physical Activity, Sleep Deprivation, and Pain Catastrophizing. The presence of CLBP has adverse effects on all these components. It was also observed that different patient reported outcomes are highly correlated with each other, and the presence of CLBP is a significant moderating factor in many of these relationships. Arrays of high-deflection strain gauges were used to collect spinal kinematic data from 274 subjects. Seven phenotypes of spinal motion were identified among study participants. Statistical analyses revealed significant differences in the patient-reported outcomes of subjects who exhibited different phenotypes. This is a promising indication that the phenotypes may also provide important information to clinicians who treat patients suffering from CLBP. Future research will be conducted to develop and identify the optimal treatments for patients according to their phenotypes, which has the potential to reduce medical costs, expedite recovery, and improve the lives of millions of patients worldwide.

Introduction: Muscle fatigue can contribute to acute flare-ups of lower back pain with associated consequences such as pain, disability, lost work time, increased healthcare utilization, and increased opioid use and potential abuse. The SPINE Sense system is a wearable device with 16 high deflection nanocomposite strain gauge sensors on kinesiology tape which is adhered to the skin of the lower back. This device is used to correlate lumbar skin strains with the motion of the lumbar vertebrae and to phenotype lumbar spine motion. In this work it was hypothesized that the SPINE Sense device can be used to detect differences in biomechanical movements consequent to muscle fatigue. A human subject study was completed with 30 subjects who performed 14 functional movements before and after fatiguing their back muscles through the Biering-Sørensen endurance test with the SPINE Sense device on their lower back collecting skin strain data. Various features from the strain gauge sensors were extracted from these data and were used as inputs to a random forest classification machine learning model. The accuracy of the model was assessed under two training/validation conditions, namely a hold-out method and a leave-one-out method. The random forest classification models were able to achieve up to 84.22% and 78.37% accuracies for the hold-out and leave-one-out methods respectively. Additionally, a system usability study was performed by presenting the device to 32 potential users (clinicians and individuals with lower back pain) of their device. They received a scripted explanation of the use of the device and were then instructed to score it with the validated System Usability Score. In addition they were given the opportunity to voice concerns, questions, and offer any other additional feedback about the design and use of the device. The average System Usability Score from all participants from the system usability study was 72.03 with suggestions of improving the robustness of electrical connections and smaller profiles of accompanying electronics. Feedback from the potential users of the device was used to make more robust electrical connections and smaller wires and electronics modules. These improvements were achieved by making a two-piece design: one piece contains the sensors on kinesiology tape that is directly attached to the patient and the other one contains the wires sewn into stretch fabric to create stretchable electronic connections to the device. It is concluded that a machine-learning model of the data from the SPINE Sense device can classify lumbar motion with sufficient accuracy for clinical utility. It is also concluded that the device is usable and intuitive to use.