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ARMA modelling and nonparametric probability density function of gait signal using L2 – L1 estimator in patients with neurodegenerative disease

We present an ARMA model with low order (LOARMA) and a nonparametric probability density function (NPDF) of the human gait signal from mixed L2 2 L1 norms to characterise and identify the neurodegenerative diseases (NDDs). Human locomotion is regulated by the central nervous system (CNS). The neurophysiological changes in the CNS due to Parkinson’s disease (PD), Huntington’s disease (HD) or amyotrophic lateral sclerosis (ALS) may cause altered gait cycle duration (stance interval: time of bilateral foot contact). Figure 1 shows an example of the altered gait cycle duration of the PD (left stance). We can see atypical points with large values (named outliers).