Insights into gait disorders: Walking variability using phase plot analysis, Parkinson's disease

Gait variability may have greater utility than spatio-temporal parameters and can, be an indication for risk of falling in people with Parkinson's disease (PD). Current methods rely on prolonged data collection in order to obtain large datasets which may be demanding to obtain. We set out to explore a phase plot variability analysis to differentiate typically developed adults (TDAs) from PD obtained from two 10 m walks.Fourteen people with PD and good mobility (Rivermead Mobility Index ≥8) and ten aged matched TDA were recruited and walked over 10-m at self-selected walking speed. An inertial measurement unit was placed over the projected centre of mass (CoM) sampling at 100 Hz. Vertical CoM excursion was derived to determine modelled spatiotemporal data after which the phase plot analysis was applied producing a cloud of datapoints. SDA described the spread and SDB the width of the cloud with β the angular vector of the data points. The ratio (∀) was defined as SDA: SDB.Cadence (p = .342) and stride length (p = .615) did not show a significance between TDA and PD. A difference was found for walking speed (p = .041). Furthermore a significant difference was found for β (p = .010), SDA (p = .004) other than SDB (p = .385) or ratio ∀ (p = .830).Two sequential 10-m walks showed no difference in PD for cadence (p = .193), stride length (p = .683), walking speed (p = .684) and β (p = .194), SDA (p = .051), SDB (p = .145) or ∀ (p = .226).The proposed phase plot analysis, performed on CoM motion could be used to reliably differentiate PD from TDA over a 10-m walk.

► We developed a phase-plot method which measures gait variability. ► We do not let this method rely on temporal or spatial parameters, but on centre of mass excursion. ► The proposed novel method does only requires a single 10-m walk. ► This method can distinguish Parkinson's from age-matched typical developed adults.