Predicting the effects of oscillator-based assistance on stride-to-stride variability of Parkinsonian walkers

Abstract

Parkinson's disease is a severe neurodegenerative disorder that affects sensorimotor control. In particular, several gait impairments are reported, including a decrease of long-range autocorrelations in stride duration time series. This complex statistics is potentially a biomarker of the risk of falling. This paper aims at developing model-based predictions about the loss of long-range autocorrelations in the gait of Parkinsonian patients, and how these autocorrelations can be restored by an oscillator-based walking assistance. Using a Super Central Pattern Generator model coupled with an adaptive oscillator, we show that this type of assistance has the potential to improve long-range autocorrelations in time series of Parkinsonian walkers. This requires however to tune the adaptive oscillator with slow learning gains, raising challenges for porting this method to an actual device.