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Levschuk, Adam Joseph

University of British Columbia

Healthy gait and balance are essential for independent living and maintaining a high quality of life. Unfortunately, people’s gait and balance may be compromised by disease, injury, and age-related factors. Immobility can lead to the loss of independence, social isolation, comorbidities, and a decreased quality of life. Traditional gait analysis requires expensive motion capture laboratories equipped with sophisticated optical motion capture systems that require the patient to be physically present in the lab. We developed a wearable sensor system containing inertial measurement units (IMU) and signal processing algorithms that are capable of performing gait analysis and balance monitoring that will circumvent the limitations of camera and floor-based gait analysis systems. 16 healthy adults completed validation testing with our wearable gait analysis system. Reliability (intraclass correlation coefficients) and validity testing (Pearson’s coefficients) were used to compare IMU measured values to ground truth values. The gait parameters calculated by our foot worn IMU system showed good to excellent reliability (ICC₂,₁ = 0.75 to 1.00) and high to very high validity (r = 0.70 to 1.00) for nearly every gait parameter compared to ground truth validated parameters. 13 healthy participants completed balance testing whereby IMUs on the upper and lower back measured 11 balance parameters during poses of increasing difficulty. One-Way ANOVA testing compared parameters between poses and Pearson’s coefficients and Mann-Whitney U testing compared parameters from IMUs on the upper and lower back. ANOVA testing revealed that for both IMUs, nearly all the balance parameters could detect significant (p < 0.05) differences between poses of increasing difficulty. Mann-Whitney U testing showed differences (p < 0.05) between parameters measured by the IMU on the upper and lower back could be found only in the easiest pose. Finite-time Lyapunov exponent (FTLE) fields were calculated for all balance data and were compared between poses using one-way ANOVA testing. One-way ANOVA testing showed significant differences (p < 0.05) in FTLE areas for three of the four comparisons made. The wearable IMU system for gait and balance monitoring is a viable candidate to supplement traditional clinical techniques and make quality rehabilitation assessment methods more accessible to everyone.

Text

2021-06-16

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/78696

Biomedical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/