Gait Asymmetry Post-Stroke: Determining Valid and Reliable Methods Using a Single Accelerometer Located on the Trunk

Abstract

Asymmetry is a cardinal symptom of gait post-stroke that is targeted during rehabilitation. Technological developments have allowed accelerometers to be a feasible tool to provide digital gait variables. Many acceleration-derived variables are proposed to measure gait asymmetry. Despite a need for accurate calculation, no consensus exists for what is the most valid and reliable variable. Using an instrumented walkway (GaitRite) as the reference standard, this study compared the validity and reliability of multiple acceleration-derived asymmetry variables. Twenty-five post-stroke participants performed repeated walks over GaitRite whilst wearing a tri-axial accelerometer (Axivity AX3) on their lower back, on two occasions, one week apart. Harmonic ratio, autocorrelation, gait symmetry index, phase plots, acceleration, and jerk root mean square were calculated from the acceleration signals. Test–retest reliability was calculated, and concurrent validity was estimated by comparison with GaitRite. The strongest concurrent validity was obtained from step regularity from the vertical signal, which also recorded excellent test–retest reliability (Spearman’s rank correlation coefficients (rho) = 0.87 and Intraclass correlation coefficient (ICC21) = 0.98, respectively). Future research should test the responsiveness of this and other step asymmetry variables to quantify change during recovery and the effect of rehabilitative interventions for consideration as digital biomarkers to quantify gait asymmetry.

A Multifactorial Model of Multiple Sclerosis Gait and Its Changes Across Different Disability Levels

“…Similarly, step regularity, which compares the acceleration signals between the left and right feet, loaded onto the asymmetry domain, whereas stride regularity loaded onto the variability domain [19], [66], [67]. It has previously been shown that in patients post-stroke, step regularity in AP and V directions is strongly correlated with asymmetry in step time and in swing time [66], with similar results also seen in people with PD [67]. Not surprisingly, both jerk and RMS metrics loaded onto the pace domain.…”

Quantitative body symmetry assessment during neurological examination

“…Their results revealed more impaired symmetry in MS than the CO group, but it was not statistically significant in symmetry indices for cycle time or stance time between repeated measurements [34]. The asymmetry of gait time parameters was also found in people who had a stroke [44].…”

Hybrid Deep-Learning Framework Based on Gaussian Fusion of Multiple Spatiotemporal Networks for Walking Gait Phase Recognition

“…erefore, accurate classification of changing walking style of human lower limbs' status is urgently required to achieve consistency and coordination of human-machine interaction [2]. An effective analysis of walking style is performed well in athletic performance improvement or disease diagnosis and rehabilitation research, which have been applied in clinical treatment plan with multiple sclerosis, Parkinson, brain trauma, and other diseases [3,4]. Note that traditional walking analysis is represented by detecting the different gait phase based on motion information (e.g., angles, speed, or acceleration) of the knees, ankles, and hips when walking or running.…”

“…Note that traditional walking analysis is represented by detecting the different gait phase based on motion information (e.g., angles, speed, or acceleration) of the knees, ankles, and hips when walking or running. For example, Buckley et al [3] used gait phase detection to diagnose stroke patients, and Achanta et al [5] used a novel hidden-Markov-based adaptive dynamic time warping to analysis gait for identifying physically challenged persons and providing them with appropriate alerts by monitoring walking. In order to make exoskeleton robots have better human-machine coordination, some researchers have begun to try program human robots, generate gait trajectories of wearers through gait phase recognition technology, and control the movement of wearable auxiliary devices, such as robotic prostheses and orthotics [6,7].…”