Unknown

Dataset Information

0

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 at-home behavior as well as scripted in-clinic exercises, it avoids differences across human evaluators, and can be used continuously without requiring scripted tasks be performed. We demonstrate the effectiveness of this technique using movement data captured with commercially available wrist worn sensors in 35 participants with Parkinson's disease in-clinic and 25 participants monitored at home.

SUBMITTER: Abrami A 

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

REPOSITORIES: biostudies-literature

altmetric image

Publications

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

Abrami Avner A   Heisig Stephen S   Ramos Vesper V   Thomas Kevin C KC   Ho Bryan K BK   Caggiano Vittorio V  

Scientific reports 20200430 1


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 s  ...[more]

Similar Datasets

| S-EPMC6308833 | biostudies-other
| S-EPMC3527185 | biostudies-literature
| S-EPMC5860058 | biostudies-literature
| S-EPMC2409071 | biostudies-literature
| S-EPMC5086096 | biostudies-literature
| S-EPMC3576377 | biostudies-literature
| S-EPMC6073521 | biostudies-literature
| S-EPMC8720470 | biostudies-literature
| S-EPMC5460211 | biostudies-literature
| S-EPMC9498573 | biostudies-literature