Available at: https://digitalcommons.calpoly.edu/theses/2773
Date of Award
6-2023
Degree Name
MS in Biomedical Engineering
Department/Program
Biomedical Engineering
College
College of Engineering
Advisor
Scott Hazelwood
Advisor Department
Biomedical Engineering
Advisor College
College of Engineering
Abstract
Residual gait abnormalities are common following lower limb injury and/or stroke and can have several negative impacts on an individual’s life. Without continuous treatment and follow up, individuals can be prone to chronic pain as abnormal gait may lead to non-physiological loading of the musculoskeletal system. The current industry gold standard for diagnosing abnormal gait requires specialty equipment that is generally only available at designated gait facilities. Due to the inaccessibility and high cost associated with these facilities, a wearable SmartSole device to continuously detect abnormal gait was proposed. A previous iteration of the SmartSole was unable to properly detect abnormal gait and also experienced fracturing throughout the 3D printed body. In this present study, sensor placement and material selection were reconsidered to address these limitations. The objective of this study was to determine if a redesigned SmartSole could identify events of abnormal gait through validation and verification testing against the industry standard force plates. In total, 14 participants were selected for gait studies, 7 with pronounced gait abnormalities (e.g. limps), and 7 with physiological gait. Parameters of interest included stance time, gait cycle time, and the ratio of the force magnitudes recorded during heel strike and toe off. Results indicated that the SmartSole was effective at determining overall event timings within the gait cycle, as both stance and cycle time had strong, positive correlations (left stance: r = 0.761, right stance: r = 0.560, left cycle: r = 0.688) with the force plates, with the exception of right foot cycle time. The sole was not effective at measuring actual values of events during gait as there were weak correlations with the force plates. Furthermore, when comparing parameters of interest between the injured and non-injured sides for test participants with gait abnormalities, neither the SmartSole nor the force plates were able to detect significant differences. The inability of the sole to accurately collect force magnitudes or to detect abnormal gait leads to the conclusion that additional sensors may need to be implemented. Future iterations may consider placement of additional sensors to allow for a “fuller picture” and the inclusion of other types of sensors for improved, continuous tracking of gait abnormalities.