Abstract
Summary
Measurement of changes in the physiological cycle-to-cycle variability in gait kinematics using the ELLIS approach holds promise as a new tool for quantitative evaluation of gait adaptability.
Introduction
Adaptability is arguably one of the most crucial factors of gait function. However, functional limitations in adaptability have not been well documented, presumably due to the inability to accurately measure this aspect. For this purpose, we developed a new method to quantify subtle changes in cycle-to-cycle physiological variability in gait kinematics; a technique designated as the entropy of leg-linkage inertial signals (ELLIS) analysis. A previous study (Tochigi et al., JOR 2012) found that the ELLIS outputs in an asymptomatic cohort) became lower with greater age, and that subjects with symptomatic knee osteoarthritis exhibited lower values compared to age-matched asymptomatic subjects. In addition, highly consistent speed-dependent increases in ELLIS outputs (in the asymptomatic subjects) were also documented. This speed-dependency is consistent with the fact that stable walking at a faster pace places higher demands on the neuromuscular control systems. Complex interactions across multiple controlling factors presumably increase perturbations to gait kinematics within the “normal” range (i.e., increase in physiological variability). To advance understanding of the degree of speed dependence, the present study aimed to test whether or not the ELLIS outputs would linearly increase with increase in walking speed.
Methods
Six asymptomatic adult individuals (all males, age 24 – 47) were recruited and completed an institutionally approved consent process. No subjects had lower limb symptoms, histories of major lower limb pathology in the prior year, or systemic conditions that might affect gait (e.g., neurological or cardiovascular impairments). For leg kinematics measurement, each subject wore a portable wireless inertial monitor, which was strapped to the lateral aspect of the left or right calf, just above the ankle. Self-selected gait speed was determined during a timed corridor walk. Data during a treadmill walk were collected at 60%, 80%, 100%, 120% and 140% of the individuals’ self-selected pace, in a randomised order. The kinematic data collected were six channels of synchronised signals (sampling rate: 150Hz), including tri-axial rotational rate and tri-axial acceleration data. For each of these two 3-D kinematic datasets, entropy was measured individually using a non-linear measure designated as Sample Entropy (SampEn). These outputs were plotted for the relationship with relative speed change, and the correlation between entropy and relative speed change was tested using the Pearson's linear regression model.
Results
The SampEn values of the rotational rate data exhibited high positive correlation with relative speed changes, as indicated by the correlation coefficients (r) > 0.95 in all subjects, while those for the acceleration data exhibited modest correlation (r: 0.66 to 0.99).
Conclusion
These data support the hypothesised speed-dependent linear increase of ELLIS outputs. Assuming the sensitivity of this speed-dependent change is associated with the integrity of gait adaptability, this approach may be capable of quantifying decrease of gait adaptability in various pathological conditions. This gait analysis technique does not require elaborate laboratory equipment, permitting data collection at a variety of non-specialised settings, such as private clinics and community-based settings. The ELLIS approach holds promise as a new convenient diagnostic tool.
