Automatic four-limb IMU gait analysis in the canine DMD model

Abstract

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Gait analysis is valuable for studying neuromuscular and skeletal diseases. Wearable motion sensors or inertial measurement units (IMUs) possess advantages that have made them common for human gait analysis. Canines, an important large animal model for translational research of human diseases, have limited information on use of IMUs. Our objective is to develop a method for accurate and reliable gait analysis in dogs using a wearable IMU. In a preliminary study, we built a wireless IMU sensor using off-the-shelf components and developed a MATLAB algorithm for data acquisition and stride timing determination. Stride parameters from 1,259 steps of three adult mixed breed dogs were determined across a range of six height-normalized speeds using the IMU system. The IMU results were validated with frame-by-frame manual counting of high-speed video recordings. Comparing IMU derived results with video revealed that the mean error [plus or minus] standard deviation for stride, stance, and swing duration was 0.001 [plus or minus] 0.025, -0.001 [plus or minus] 0.030, and 0.001 [plus or minus] 0.019 s respectively. A mean error [plus or minus] standard deviation of 0.000 [plus or minus] 0.020 and -0.008 [plus or minus] 0.027 s was obtained for determining toe-off and toe-touch events respectively. Only one step was missed by the algorithm in the video dataset of 1,259 steps. In a subsequent study, we developed a four-limb, four-device, gait analysis system. The new system incorporated a specialized railway procedure, a convenient and reliable sensor mounting bracket, and multiple-limb orientation tracking in the form of Euler angles. A data analysis technique was developed using linear-length normalization of gait cycles and bivariate phase portraits. Preliminary findings in dystrophic canines at 1 and 2 years of age indicate unique and previously unquantified characteristics of the dystrophic gait. A more in-depth analysis of our dataset is warranted to evolve the gait analysis method. In summary, we have developed an IMU-based, four-limb, four-device gait analysis methodology for automatic canine gait analysis. The method can be used for studying neuromuscular diseases in veterinary clinics and in translational research.