7,663 research outputs found
Rhythmic dynamics and synchronization via dimensionality reduction : application to human gait
Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis. However, the inherent oscillation and ubiquity of noise in such non-strictly periodic signals pose great challenges to current methodologies. To this end, we exploit the state-of-the-art technology in pattern recognition and, specifically, dimensionality reduction techniques, and propose to reconstruct and characterize the dynamics accurately on the cycle scale of the signal. This is achieved by deriving a low-dimensional representation of the cycles through global optimization, which effectively preserves the topology of the cycles that are embedded in a high-dimensional Euclidian space. Our approach demonstrates a clear advantage in capturing the intrinsic dynamics and probing the subtle synchronization patterns from uni/bivariate oscillatory signals over traditional methods. Application to human gait data for healthy subjects and diabetics reveals a significant difference in the dynamics of ankle movements and ankle-knee coordination, but not in knee movements. These results indicate that the impaired sensory feedback from the feet due to diabetes does not influence the knee movement in general, and that normal human walking is not critically dependent on the feedback from the peripheral nervous system
Stratification of the phase clouds and statistical effects of the non-Markovity in chaotic time series of human gait for healthy people and Parkinson patients
In this work we develop a new method of diagnosing the nervous system
diseases and a new approach in studying human gait dynamics with the help of
the theory of discrete non-Markov random processes. The stratification of the
phase clouds and the statistical non-Markov effects in the time series of the
dynamics of human gait are considered. We carried out the comparative analysis
of the data of four age groups of healthy people: children (from 3 to 10 year
olds), teenagers (from 11 to 14 year oulds), young people (from 21 up to 29
year oulds), elderly persons (from 71 to 77 year olds) and Parkinson patients.
The full data set are analyzed with the help of the phase portraits of the four
dynamic variables, the power spectra of the initial time correlation function
and the memory functions of junior orders, the three first points in the
spectra of the statistical non-Markov parameter. The received results allow to
define the predisposition of the probationers to deflections in the central
nervous system caused by Parkinson's disease. We have found out distinct
differencies between the five submitted groups. On this basis we offer a new
method of diagnostics and forecasting Parkinson's disease.Comment: 15 pages, 5 figs, 3 Table
Regular and stochastic behavior of Parkinsonian pathological tremor signals
Regular and stochastic behavior in the time series of Parkinsonian
pathological tremor velocity is studied on the basis of the statistical theory
of discrete non-Markov stochastic processes and flicker-noise spectroscopy. We
have developed a new method of analyzing and diagnosing Parkinson's disease
(PD) by taking into consideration discreteness, fluctuations, long- and
short-range correlations, regular and stochastic behavior, Markov and
non-Markov effects and dynamic alternation of relaxation modes in the initial
time signals. The spectrum of the statistical non-Markovity parameter reflects
Markovity and non-Markovity in the initial time series of tremor. The
relaxation and kinetic parameters used in the method allow us to estimate the
relaxation scales of diverse scenarios of the time signals produced by the
patient in various dynamic states. The local time behavior of the initial time
correlation function and the first point of the non-Markovity parameter give
detailed information about the variation of pathological tremor in the local
regions of the time series. The obtained results can be used to find the most
effective method of reducing or suppressing pathological tremor in each
individual case of a PD patient. Generally, the method allows one to assess the
efficacy of the medical treatment for a group of PD patients.Comment: 39 pages, 10 figures, 1 table Physica A, in pres
Persistent Homology of Attractors For Action Recognition
In this paper, we propose a novel framework for dynamical analysis of human
actions from 3D motion capture data using topological data analysis. We model
human actions using the topological features of the attractor of the dynamical
system. We reconstruct the phase-space of time series corresponding to actions
using time-delay embedding, and compute the persistent homology of the
phase-space reconstruction. In order to better represent the topological
properties of the phase-space, we incorporate the temporal adjacency
information when computing the homology groups. The persistence of these
homology groups encoded using persistence diagrams are used as features for the
actions. Our experiments with action recognition using these features
demonstrate that the proposed approach outperforms other baseline methods.Comment: 5 pages, Under review in International Conference on Image Processin
Sample Efficient Optimization for Learning Controllers for Bipedal Locomotion
Learning policies for bipedal locomotion can be difficult, as experiments are
expensive and simulation does not usually transfer well to hardware. To counter
this, we need al- gorithms that are sample efficient and inherently safe.
Bayesian Optimization is a powerful sample-efficient tool for optimizing
non-convex black-box functions. However, its performance can degrade in higher
dimensions. We develop a distance metric for bipedal locomotion that enhances
the sample-efficiency of Bayesian Optimization and use it to train a 16
dimensional neuromuscular model for planar walking. This distance metric
reflects some basic gait features of healthy walking and helps us quickly
eliminate a majority of unstable controllers. With our approach we can learn
policies for walking in less than 100 trials for a range of challenging
settings. In simulation, we show results on two different costs and on various
terrains including rough ground and ramps, sloping upwards and downwards. We
also perturb our models with unknown inertial disturbances analogous with
differences between simulation and hardware. These results are promising, as
they indicate that this method can potentially be used to learn control
policies on hardware.Comment: To appear in International Conference on Humanoid Robots (Humanoids
'2016), IEEE-RAS. (Rika Antonova and Akshara Rai contributed equally
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