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.
| Identifer | oai:union.ndltd.org:BGMYU2/oai:scholarsarchive.byu.edu:etd-11471 |
| Date | 11 June 2024 |
| Creators | Bischoff, Brianna |
| Publisher | BYU ScholarsArchive |
| Source Sets | Brigham Young University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | https://lib.byu.edu/about/copyright/ |
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