Figure 1.
A) Schematic depiction of the WalkMate system. B) The computer's timing system used nonlinear oscillators and was organized hierarchically in two modules. Module 1 mutually entrained the gait frequencies of the computer and the participant. Module 2 adjusted the relative phase difference between the computer's auditory onset and the participant's step contact to a target phase difference [more details in the Materials and Methods section].
Figure 2.
On the left, the stride times of one leg are plotted against trial time. On the right, the DFA technique plots the average fluctuation per box size. Using DFA, a scaling exponent α≈0.5 corresponds to rough and unpredictable white noise; α≈1.0 corresponds to 1/f-like noise and long-range correlations [26]. The mean and SD of stride times are similar in both trials, but the fractal scaling differs considerably. During the Silent condition (A), the PD patient's strides are unpredictable and akin to white noise, whereas during interactive rhythmic stimulation (B), the stride fluctuations have a 1/f-like structure.
Figure 3.
DFA fractal-scaling exponent results by condition.
A) Parkinson's patients during rhythmic treatment, B) healthy participants during rhythmic treatment, and C) Parkinson's patients carry-over effect during a silent trial five minutes after the rhythmic treatment. The cueing conditions are unassisted Silent Control; interactive WalkMate rhythmic auditory stimulation; and Fixed-tempo rhythmic auditory stimulation (RAS). Error bars represent ± SEM. *p<.05; n.s. = non-significant.
Table 1.
Stride Times (Mean and Standard Deviation) of one leg in seconds for Parkinson's patients (PD) and healthy participants with rhythmic cueing treatment, and for the PD patients' post-treatment carry-over.