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

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.

Aging with Autism Departs Greatly from Typical Aging

“…the model also proposes that studying autism across these levels may help us stratify the heterogeneity of autism across aging. Over years of peer reviewed work, using this model has served to reveal several stochastic features of voluntary [2,14], spontaneous [15], involuntary [15][16][17][18] and autonomic [19,20] motions, offering classification power in autism and other neuropsychiatric and neurological conditions (e.g., schizophrenia [21], Parkinson's disease [22][23][24][25], neuropathies [26] and impairments of the nervous systems due to traumatic brain injury inducing coma [27] or stroke [28]). [29], with permission from Elsevier).…”

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

“…We also examine automatic motions and the system's capacity for physical entrainment with the metronome's beats per minute. ) from the ideal GOAL of low noise-to-signal ratio (low dispersion) and high predictability (symmetric distribution) found in neurotypicals; away from poor feedback (random noise) found in more advanced PD, deafferented patients 24,25,26,27 , schizophrenia 28 and autistic individuals 3,18,22,29 . (H) Simplified visualization to represent these stochastic states evolving in time is based on the power law relationship between the shape and scale parameters.…”

“…The results from the stochastic analysis of the walking task are depicted in Figure 12. Since all cognitive and memory tasks can be performed while the computer's webcam records the face, it is possible to use OpenPose, an open source machine learning software that is openly available to researchers 35 , and extract the facial information, which can be used to infer information related to sentiment or emotional content. Often in PD the facial expressions decrease as the dopamine depletion may eventually result in low muscle tone.…”

“…Currently, there are efforts in collecting such digital data on a larger scale such as the mPower app study from University of Rochester (https:// parkinsonmpower.org) and Kaggle. Indeed, using these open-access data repositories, we were able to stratify PD and normal aging individuals from accelerometer data obtained from mobile phones, and to automatically classify activities that are embedded in the clinical tests presented here 35 .…”

Technical and clinical view on ambulatory assessment in Parkinson's disease

“…The next step in this evolution process has been the seminal use of smartphones for the purpose of data collection in daily life. As these devices have internal IMUs, for example, to adapt the display position to vertical and horizontal position of the smartphone, they can also be used for quantitative movement analysis studies relevant to PD, such as tremor (56), gait (57), more complex axial movements including transfers and turns (58), physical activity monitoring (59)(60)(61)(62)(63)(64), and falls detection combined with telemedicine aspects (65,66).…”