Academic Journal
Application of High-Deflection Strain Gauges to Characterize Spinal-Motion Phenotypes Among Patients with CLBP
| العنوان: | Application of High-Deflection Strain Gauges to Characterize Spinal-Motion Phenotypes Among Patients with CLBP |
|---|---|
| المؤلفون: | Baker, Spencer Alan |
| المصدر: | Theses and Dissertations |
| بيانات النشر: | BYU ScholarsArchive |
| سنة النشر: | 2024 |
| المجموعة: | Brigham Young University (BYU): ScholarsArchive |
| مصطلحات موضوعية: | nanocomposites, high-deflection strain gauges, modeling, patient-reported outcomes, phenotyping, machine learning, chronic low back pain, Engineering |
| الوصف: | 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 ... |
| نوع الوثيقة: | text |
| وصف الملف: | application/pdf |
| اللغة: | unknown |
| Relation: | https://scholarsarchive.byu.edu/etd/10293; https://scholarsarchive.byu.edu/context/etd/article/11302/viewcontent/1302739022333920200402_etd.pdf |
| الاتاحة: | https://scholarsarchive.byu.edu/etd/10293 https://scholarsarchive.byu.edu/context/etd/article/11302/viewcontent/1302739022333920200402_etd.pdf |
| Rights: | https://lib.byu.edu/about/copyright/ |
| رقم الانضمام: | edsbas.85F0FB09 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |