Effects of arm swing amplitude and lower limb asymmetry on motor variability patterns during treadmill gait

Abstract

Motor variability is a fundamental feature of gait. Altered arm swing and lower limb asymmetry (LLA) may be contributing factors having been shown to affect the magnitude and dynamics of variability in spatiotemporal and trunk motion. However, the effects on lower limb joints remain unclear. Full-body kinematics of 15 healthy young adults were recorded during treadmill walking using the Computer-Assisted Rehabilitation Environment system. Participants completed six trials, combining three arm swing (AS) amplitude (normal, active, held) and two LLA (symmetrical, asymmetrical) conditions. The mean standard deviation (meanSD), maximum Lyapunov exponent (λmax), detrended fluctuation analysis scaling exponent of range of motion (DFAα), and sample entropy (SaEn) were computed for tridimensional trunk, pelvis, and lower limb joint angles, and compared using repeated-measures ANOVAs. Relative to normal AS, active AS increased meanSD of all joint angles, λmax of frontal plane hip and ankle angles, and SaEn of sagittal plane ankle angles. Active AS, however, did not affect λmax or SaEn of trunk or pelvis angles. LLA increased meanSD of sagittal plane joint angles, λmax of Euclidean norm trunk angle and of lower limb joint angles, and SaEn of ankle dorsiflexion/ plantarflexion, but decreased SaEn of tridimensional trunk angles and hip rotation in the slower moving leg. Alterations in lower limb variability with active AS and LLA suggest that young adults actively exploit their lower limb redundancies to maintain gait. This appears to preserve trunk stability and regularity during active AS but not during LLA.

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

“…Relative to gait at preferred speed and with preferred arm swing, responses to changes in speed and arm swing amplitude were detected identically between models for 28/48 comparisons for ROM, 29/48 comparisons for meanSD, and 35/48 comparisons for λ max . Model-detected responses are in agreement with several previous findings, including increases in joint ROM with fast speed and decreases with slow speed [52], increases in SD of trunk kinematics and meanSD of lower-limb angles with active arm swing [36,37], and increases in λ max of hip abduction with active arm swing [37]. Although our trunk motion responses appear to disagree with reported increases in meanSD with fast speed [33], increases in λ max with fast speed [34], and decreases in λ max with active arm swing [35], differences can be attributed to the reference frame of the trunk (relative to the pelvis in our study vs. relative to ground in [33]) and to the sensitivity of the λ max state-space to different inputs [53] (time-delayed joint angles in our study vs. velocities and accelerations [34] vs. timedelayed velocities [35]).…”

“…These outcomes were more frequently overestimated by the IMU model, with higher meanSD (0.06-0.56°, Figure 4) and λ max (0.20-0.65, Figure 5) for most angles, higher DFAα of ankle AA (0.12, Figure 6), and higher SaEn of trunk AA, trunk IE, pelvis FE, pelvis IE, hip FE, and ankle FE (0.04-0.28, Figure 7). These biases approximated the optoelectronic-measured inter-individual standard deviations in our sample and in measurements from other studies for joint angle ROM [47], meanSD [33,37], DFAα [37], and SaEn [5,37], but exceeded inter-individual standard deviations for joint angle λ max [4,37]. The Bland-Altman plots (Figures 3-7) show that nearly all measurement differences…”

“…This sequence was repeated for five trials, each under a different gait condition that varied by gait speed and/or arm swing magnitude: 1) preferred gait speed and arm swing; 2) 70% preferred gait speed, preferred arm swing; 3) 130% preferred gait speed, preferred arm swing; 4) preferred gait speed, active arm swing (the participant was instructed to swing their arms such that forward swing peaked when the arm was horizontal); and 5) preferred gait speed, arms bound to the torso (using straps across the arms and across the elbows). Different gait speeds and arm swing amplitudes were evaluated since they have been shown to alter stride-to-stride variability patterns in gait [33][34][35][36][37], providing a basis for exploring the sensitivity of the IMUbased model. Condition order was randomized, the participant rested for a minimum of three minutes between trials, and optoelectronic and IMU data were continuously sampled during each trial.…”