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.