Abstract

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-SÃ¸rensen 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.

Degree

College and Department

Ira A. Fulton College of Engineering; Mechanical Engineering

Rights

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