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.

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.

Lower back pain is a widespread problem affecting millions worldwide, because understanding its development and effective treatment remains challenging. Current treatment success is often evaluated using patient-reported outcomes, which tend to be qualitative and subjective in nature, making objective success measurement difficult. Wearable sensors can provide quantitative measurements, thereby helping physicians improve care for countless individuals around the world. These sensors also have the potential to provide longitudinal data on daily motion patterns, aiding in monitoring the progress of treatment plans for lower back pain. In this work it was hypothesized that a new wearable sensor garment that makes use of high-deflection strain gauge technology--called the Z-SPINE System--will be capable of collecting biomechanical information capable of detecting characteristics of motion associated with chronic lower back pain from subjects as compared to skin-adhered wearable sensor systems. The initial prototyping development of the Z-SPINE System focused on optimizing the device's conformity to the skin, as well as the ease of use and comfortability of the design. Preliminary motion capture tests concluded that a waist belt made of an elastic four way stretch material with silicone patches and no ribbing had the highest skin conformity of the garment types tested, and further design decisions were made utilizing this knowledge. A human subject study was conducted with 30 subjects who performed 14 functional movements with both the Z-SPINE System, and the SPINE Sense System--a pre-existing wearable sensor system that utilizes the same high-deflection strain gauge technology and is adhered directly to the back. Multiple features were extracted from the strain sensor datasets for use in machine learning modeling, where the model was trained to distinguish the different movements from each other. The accuracy of the model was assessed using 4 different category number variations--two 4 category, one 7 category, and one 13 category variation. Four different machine learning models were used, with the random forest classifier generally performing the best, yielding prediction accuracies of 85.95% for the SPINE Sense System data, and 71.23% for the Z-SPINE System data in the 4 category tests. As an additional part of the human subject study, the usability of the Z-SPINE System was also assessed. Each participant filled out a system usability scale questionnaire in regards to their opinion and experience with the system after having used it; the average score given by participants was 83.4, with general feedback consisting of positive remarks about the comfort and ease of use of the current design and suggestions for improving the battery placement and fit of the Z-SPINE system. It is concluded that a machine learning model of the data from the Z-SPINE System can identify biomechanical motion with reasonable accuracy as compared to a skin-adhered wearable sensor system when the number of categories is limited. It is also concluded that the system is simple and intuitive to use.

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.