18 research outputs found
Language Time Series Analysis
We use the Detrended Fluctuation Analysis (DFA) and the Grassberger-Proccacia
analysis (GP) methods in order to study language characteristics. Despite that
we construct our signals using only word lengths or word frequencies, excluding
in this way huge amount of information from language, the application of
Grassberger- Proccacia (GP) analysis indicates that linguistic signals may be
considered as the manifestation of a complex system of high dimensionality,
different from random signals or systems of low dimensionality such as the
earth climate. The DFA method is additionally able to distinguish a natural
language signal from a computer code signal. This last result may be useful in
the field of cryptography.Comment: 21 pages, 5 figures, accepted in Physica
A classification system for assessment and home monitoring of tremor in patients with Parkinson's disease
A classification system for assessment and home monitoring of tremor in patients with Parkinson's disease
A new semi-supervised approach for characterizing the Arabic on-line handwriting of Parkinson's disease patients
Let’s draw:detecting and measuring Parkinson’s disease on smartphones
Abstract
Spiral drawing has been utilized for years as a clinical tool to observe tremors and other abnormal movements in the assessment of different movement disorders. Specifically, in Parkinson’s Disease (PD), patients’ motor functionalities are measured by various tests, and spiral drawing is one of the proven techniques for assessing the severity of PD motor symptoms. Traditionally, this test is performed on pen and paper, and visually assessed by a clinician. There have been successful efforts for digitizing this test on tablets. Here, we describe a smartphone-based digitized version of the spiral drawing test. Moreover, we introduce a square-shaped drawing to solve an identified challenge of a smaller screen estate: finger occlusion while drawing. Both approaches are evaluated with 8 Parkinson’s Disease patients and 6 age-matching control participants. Based on earlier studies and our data, we select suitable motion parameters for quantifying the task. Our results show an observable, statistically difference in performance between users with Parkinson’s Disease and the control group in drawing accuracy