Using an unbiased symbolic movement representation to characterize Parkinson's disease states.

ABSTRACT: Unconstrained human movement can be broken down into a series of stereotyped motifs or 'syllables' in an unsupervised fashion. Sequences of these syllables can be represented by symbols and characterized by a statistical grammar which varies with external situational context and internal neurological state. By first constructing a Markov chain from the transitions between these syllables then calculating the stationary distribution of this chain, we estimate the overall severity of Parkinson's symptoms by capturing the increasingly disorganized transitions between syllables as motor impairment increases. Comparing stationary distributions of movement syllables has several advantages over traditional neurologist administered in-clinic assessments. This technique can be used on unconstrained

SUBMITTER: Abrami A 

PROVIDER: S-EPMC7193555 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature