54,730 research outputs found
Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking
This manuscript presents control of a high-DOF fully actuated lower-limb
exoskeleton for paraplegic individuals. The key novelty is the ability for the
user to walk without the use of crutches or other external means of
stabilization. We harness the power of modern optimization techniques and
supervised machine learning to develop a smooth feedback control policy that
provides robust velocity regulation and perturbation rejection. Preliminary
evaluation of the stability and robustness of the proposed approach is
demonstrated through the Gazebo simulation environment. In addition,
preliminary experimental results with (complete) paraplegic individuals are
included for the previous version of the controller.Comment: Submitted to IEEE Control System Magazine. This version addresses
reviewers' concerns about the robustness of the algorithm and the motivation
for using such exoskeleton
DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways
Clinical researchers use disease progression models to understand patient
status and characterize progression patterns from longitudinal health records.
One approach for disease progression modeling is to describe patient status
using a small number of states that represent distinctive distributions over a
set of observed measures. Hidden Markov models (HMMs) and its variants are a
class of models that both discover these states and make inferences of health
states for patients. Despite the advantages of using the algorithms for
discovering interesting patterns, it still remains challenging for medical
experts to interpret model outputs, understand complex modeling parameters, and
clinically make sense of the patterns. To tackle these problems, we conducted a
design study with clinical scientists, statisticians, and visualization
experts, with the goal to investigate disease progression pathways of chronic
diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's
disease, and chronic obstructive pulmonary disease (COPD). As a result, we
introduce DPVis which seamlessly integrates model parameters and outcomes of
HMMs into interpretable and interactive visualizations. In this study, we
demonstrate that DPVis is successful in evaluating disease progression models,
visually summarizing disease states, interactively exploring disease
progression patterns, and building, analyzing, and comparing clinically
relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic
Computation Environments, An Interactive Semantics for Turing Machines (which P is not equal to NP considering it)
To scrutinize notions of computation and time complexity, we introduce and
formally define an interactive model for computation that we call it the
\emph{computation environment}. A computation environment consists of two main
parts: i) a universal processor and ii) a computist who uses the computability
power of the universal processor to perform effective procedures. The notion of
computation finds it meaning, for the computist, through his
\underline{interaction} with the universal processor.
We are interested in those computation environments which can be considered
as alternative for the real computation environment that the human being is its
computist. These computation environments must have two properties: 1- being
physically plausible, and 2- being enough powerful.
Based on Copeland' criteria for effective procedures, we define what a
\emph{physically plausible} computation environment is.
We construct two \emph{physically plausible} and \emph{enough powerful}
computation environments: 1- the Turing computation environment, denoted by
, and 2- a persistently evolutionary computation environment, denoted by
, which persistently evolve in the course of executing the computations.
We prove that the equality of complexity classes and
in the computation environment conflicts with the
\underline{free will} of the computist.
We provide an axiomatic system for Turing computability and
prove that ignoring just one of the axiom of , it would not be
possible to derive from the rest of axioms.
We prove that the computist who lives inside the environment , can never
be confident that whether he lives in a static environment or a persistently
evolutionary one.Comment: 33 pages, interactive computation, P vs N
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