Managing variability in the summary and comparison of gait data

Abstract

Variability in quantitative gait data arises from many potential sources, including natural temporal dynamics of neuromotor control, pathologies of the neurological or musculoskeletal systems, the effects of aging, as well as variations in the external environment, assistive devices, instrumentation or data collection methodologies. In light of this variability, unidimensional, cycle-based gait variables such as stride period should be viewed as random variables and prototypical single-cycle kinematic or kinetic curves ought to be considered as random functions of time. Within this framework, we exemplify some practical solutions to a number of commonly encountered analytical challenges in dealing with gait variability. On the topic of univariate gait variables, robust estimation is proposed as a means of coping with contaminated gait data, and the summary of nonnormally distributed gait data is demonstrated by way of empirical examples. On the summary of gait curves, we discuss methods to manage undesirable phase variation and non-robust spread estimates. To overcome the limitations of conventional comparisons among curve landmarks or parameters, we propose as a viable alternative, the combination of curve registration, robust estimation, and formal statistical testing of curves as coherent units. On the basis of these discussions, we provide heuristic guidelines for the summary of gait variables and the comparison of gait curves.

Assessing the stability of human locomotion: a review of current measures

“…Recently, the median absolute deviation was proposed as a measure for gait variability [126]. In general, the median absolute deviation is more robust than the standard deviation, and may thus be a good choice to use as an indicator of variability [126].…”

“…In general, the median absolute deviation is more robust than the standard deviation, and may thus be a good choice to use as an indicator of variability [126].…”

The combined influence of task accuracy and pace on motor variability in a standardised repetitive precision task

“…Chau, Young, and Redekop 2005). Similarly, time-normalised phase was calculated and expressed in terms of 11 samples/cycle, where phase at any time instant was defined according to (Hamill et al 1999):…”

A Pilot Study of Gait Function in Farmworkers in Eastern North Carolina

“…20–22 Recent studies have drawn particular attention to gait variability; that is, fluctuations in gait characteristics from one step to the next, as an indicator of gait stability. 23–25 For example, irregularity in stride length and step length are more sensitive in predicting future falls and impaired mobility than average stride length and average step length. 26–28 Additionally, gait analysis has been used to help diagnose and determine the severity of neurological conditions such as dementia 29 and Parkinson’s disease.…”