Trial reportScientific reports2020
Using an unbiased symbolic movement representation to characterize Parkinson's disease states.
Trial report in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Movement Disorders and Smart Wrist Devices: A Comprehensive Study.Sensors (Basel, Switzerland) · 2025Pooled it
- Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review.Journal of medical Internet research · 2022Pooled it
- Comparison of feetme insoles with a motion capture system coupled to force plates for assessing gait and posture.Scientific reports · 2025Article
- A Computational Ethology Approach for Characterizing Behavioral Dynamics in Bipolar Disorder.medRxiv : the preprint server for health sciences · 2025Article
- Assessing the clinical utility of inertial sensors for home monitoring in Parkinson's disease: a comprehensive review.NPJ Parkinson's disease · 2024Article
- The role of wrist-worn technology in the management of Parkinson's disease in daily life: A narrative review.Frontiers in neuroinformatics · 2023Review
- Evaluation of the Validity and Reliability of Connected Insoles to Measure Gait Parameters in Healthy Adults.Sensors (Basel, Switzerland) · 2021Article
- Will Artificial Intelligence Outperform the Clinical Neurologist in the Near Future? Yes.Movement disorders clinical practice · 2021Article
- Ensemble deep model for continuous estimation of Unified Parkinson's Disease Rating Scale III.Biomedical engineering online · 2021Article
- Automated Computer Vision Assessment of Hypomimia in Parkinson Disease: Proof-of-Principle Pilot Study.Journal of medical Internet research · 2021Article
- Parkinson's disease medication state and severity assessment based on coordination during walking.PloS one · 2021Article
- Classification of Parkinson's disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach.Journal of neuroengineering and rehabilitation · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.