Validity and sensitivity of an inertial measurement unit-driven biomechanical model of motor variability for gait

Abstract

Motor variability in gait is frequently linked to fall risk, yet field-based biomechanical joint evaluations are scarce. We evaluated the validity and sensitivity of an inertial measurement unit (IMU)-driven biomechanical model of joint angle variability for gait. Fourteen healthy young adults completed seven-minute trials of treadmill gait at several speeds and arm swing amplitudes. Joint kinematics were estimated by IMU- and optoelectronic-based models using OpenSim. We calculated range of motion (ROM), magnitude of variability (meanSD), local dynamic stability (λmax), persistence of ROM fluctuations (DFAα), and regularity (SaEn) of each angle over 200 continuous strides, and evaluated model accuracy (e.g., RMSD: root mean square difference), consistency (ICC2,1: intraclass correlation), biases, limits of agreement, and sensitivity to within-participant gait responses (effects of Speed and Swing). RMSDs of joint angles were 1.7–7.5° (pooled mean of 4.8°), excluding ankle inversion. ICCs were mostly good–excellent in the primary plane of motion for ROM and in all planes for meanSD and λmax, but were poor–moderate for DFAα and SaEn. Modeled Speed and Swing responses for ROM, meanSD, and λmax were similar. Results suggest that the IMU-driven model is valid and sensitive for field-based assessments of joint angles and several motor variability features.

Smartwatch-based prediction of single-stride and stride-to-stride gait outcomes using regression-based machine learning

“…The optoelectronic system comprised 11 passive infrared cameras (Vantage, Vicon, Oxford, UK) and spherical retroreflective markers. Calibration and tracking markers were placed on each participant's body according to a full-body marker set for gait 2,34 with rigid-body clusters of four markers on the trunk, arms, forearms, thighs, and shanks. Marker positions were sampled at 60 Hz using motion capture software (Nexus 2.11, Vicon, Oxford, UK).…”

“…Using Vicon Nexus, marker trajectories were labelled, gap-filled with a Woltring spline 45 , and low-pass filtered at 10 Hz. Processed marker trajectories were imported into OpenSim v4.2 38 to simulate full-body motion 2 . A generic skeletal model 34 was scaled to the participant by the anatomical marker positions in the static calibration and an inverse kinematic analysis was performed by minimizing the sum of weighted squared distance errors between pairs of experimental and model markers.…”

“…Several open challenges exist in the use of instrumented insoles, including user comfort, compatibility in different footwear, and usability in different environmental conditions 40 . An alternative option to insoles are body-worn IMUs, which have been used to calculate spatial and temporal gait features 2,33,35,36,41,44 . Using a single foot IMU, Rebula et al 35 developed an algorithm combining stride segmentation, drift correction, and foot trajectory formation that, relative to optoelectronic motion capture, predicted stride length and duration with a 1% difference and stride-to-stride length and width variability with a 4% difference.…”

“…Using a single trunk IMU, de Ridder and colleagues 36 demonstrated validity and intra-day reliability in measuring gait speed, cadence, stride length, and stride time. IMUs can also be instrumented on multiple lower limb segments and combined with biomechanical constraints to model foot trajectories and extract spatiotemporal parameters 2,41 . A major challenge with each of these IMU approaches is that the sensors are not positioned on locations preferred by most individuals, as exemplified in a recent investigation of persons with Parkinson's disease 32 , questioning the feasibility of these IMU positions for long-term continuous monitoring.…”