The rates of change of the stochastic trajectories of acceleration variability are a good predictor of normal aging and of the stage of Parkinson's disease.
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Yet this trend breaks down in PD where the statistical signatures seem to be a more relevant predictor of the stage of the disease.Those patients at a later stage of the disease have more random and noisier patterns than those in the earlier stages, whose statistics resemble those of the older NC.Overall the peak rates of change of the stochastic trajectories of the accelerometer are a good predictor of the stage of PD and of the age of a "normally" aging individual.
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PubMed Central - PubMed
Affiliation: Psychology Department, Computer Science, Cognitive Science, Sensory Motor Integration, Rutgers University Piscataway, NJ, USA.
ABSTRACT
The accelerometer data from mobile smart phones provide stochastic trajectories that change over time. This rate of change is unique to each person and can be well-characterized by the continuous two-parameter family of Gamma probability distributions. Accordingly, on the Gamma plane each participant can be uniquely localized by the shape and the scale parameters of the Gamma probability distribution. The scatter of such points contains information that can unambiguously separate the normal controls (NC) from those patients with Parkinson's disease (PD) that are at a later stage of the disease. In general normal aging seems conducive of more predictable patterns of variation in the accelerometer data. Yet this trend breaks down in PD where the statistical signatures seem to be a more relevant predictor of the stage of the disease. Those patients at a later stage of the disease have more random and noisier patterns than those in the earlier stages, whose statistics resemble those of the older NC. Overall the peak rates of change of the stochastic trajectories of the accelerometer are a good predictor of the stage of PD and of the age of a "normally" aging individual. No MeSH data available. Related in: MedlinePlus |
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Mentions: The 100-value sliding window for data entries proved stable across the set of participants. When hourly daily sessions were very dense we could also obtain wider sampling windows without affecting the final outcome of the analyses. For example, Figure 1 shows selected histograms from 2 consecutive days for a PD patient (Cherry) with 1000+ points used in each histogram for the estimation of several points of a segment of the overall stochastic trajectory. Using instead the 100-basic unit size increased the density of points in a given segment of the overall trajectory (e.g., the small segment shown in Figure 1C would have more points but would keep the general trend). Sampling 1000+ points did not change the overall trend of the patterns in the longer stochastic trajectory of a day (e.g., as the one shown in Figure 2A), but it would improve the estimation by lowering the errors and tightening the confidence intervals. |
View Article: PubMed Central - PubMed
Affiliation: Psychology Department, Computer Science, Cognitive Science, Sensory Motor Integration, Rutgers University Piscataway, NJ, USA.
No MeSH data available.