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Affordable stroke therapy in high-, low- and middle-income countries: From Theradrive to Rehab CARES, a compact robot gym

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Journal of rehabilitation and assistive technologies engineering 2017年 4卷 2055668317708732-2055668317708732页

作者： Michelle Jillian Johnson Roshan Rai Sarath Barathi Rochelle Mendonca Karla Bustamante-Valles Department of Physical Medicine and Rehabilitation University of Pennsylvania PA USA. Department of Biomedical Engineering University of Pennsylvania PA USA. General Robotics Automation Sensing and Perception (GRASP) University of Pennsylvania PA USA. Occupational Therapy Temple University PA USA. Chihuahua Mexico instead of Monterrey Mexico. Orthopaedic and Rehabilitation Engineering Center Marquette University Milwaukee WI USA.

Affordable technology-assisted stroke rehabilitation approaches can improve access to rehabilitation for low-resource environments characterized by the limited availability of rehabilitation experts and poor rehabilitation infrastructure. This paper describes the evolution of an approach to the implementation of affordable, technology-assisted stroke rehabilitation which relies on low-cost mechatronic/robot devices integrated with off-the-shelf or custom games. Important lessons learned from the evolution and use of Theradrive in the USA and in Mexico are briefly described. We present how a stronger and more compact version of the Theradrive is leveraged in the development of a new low-cost, all-in-one robot gym with four exercise stations for upper and lower limb therapy called Rehab Community-based Affordable Robot Exercise System (Rehab C.A.R.E.S). Three of the exercise stations are designed to accommodate versions of the 1 DOF haptic Theradrive with different custom handles or off-the-shelf commercial motion machine. The fourth station leverages a unique configuration of Wii-boards. Overall, results from testing versions of Theradrive in USA and Mexico in a robot gym suggest that the resulting presentation of the Rehab C.A.R.E.S robot gym can be deployed as an affordable computer/robot-assisted solution for stroke rehabilitation in developed and developing countries.

关键词： Design global health haptic high-income countries low- and middle-income countries motivation neurorehabilitation rehabilitation robot therapy stroke

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Switching to Learn

Switching to Learn

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American Control Conference

作者： Shahin Shahrampour Mohammad Amin Rahimian Ali Jadbabaie Department of Electrical and Systems Engineering and General Robotics Automation Sensing and Perception (GRASP) Laboratory University of Pennsylvania Philadelphia PA 19104-6228 USA

ISBN: (纸本)9781479917730

A network of agents attempt to learn some unknown state of the world drawn by nature from a finite set. Agents observe private signals conditioned on the true state, and form beliefs about the unknown state accordingly. Each agent may face an identification problem in the sense that she cannot distinguish the truth in isolation. However, by communicating with each other, agents are able to benefit from side observations to learn the truth collectively. Unlike many distributed algorithms which rely on all-time communication protocols, we propose an efficient method by switching between Bayesian and non-Bayesian regimes. In this model, agents exchange information only when their private signals are not informative enough;thence, by switching between the two regimes, agents efficiently learn the truth using only a few rounds of communications. The proposed algorithm preserves learnability while incurring a lower communication cost. We also verify our theoretical findings by simulation examples.

关键词： Switches Bayes methods Convergence Robot sensing systems Symmetric matrices Protocols Silicon

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Exponentially Fast Parameter Estimation in Networks Using Distributed Dual Averaging

Exponentially Fast Parameter Estimation in Networks Using Di...

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IEEE Annual Conference on Decision and Control

作者： Shahin Shahrampour Ali Jadbabaie Department of Electrical and Systems Engineering and General Robotics Automation Sensing and Perception (GRASP) Laboratory University of Pennsylvania Philadelphia PA 19104-6228 USA

ISBN: (纸本)9781467357159

In this paper we present an optimization-based view of distributed parameter estimation and observational social learning in networks. Agents receive a sequence of random, independent and identically distributed (i.i.d.) signals, each of which individually may not be informative about the underlying true state, but the signals together are globally informative enough to make the true state identifiable. Using an optimization-based characterization of Bayesian learning as proximal stochastic gradient descent (with Kullback-Leibler divergence from a prior as a proximal function), we show how to efficiently use a distributed, online variant of Nesterov's dual averaging method to solve the estimation with purely local information. When the true state is globally identifiable, and the network is connected, we prove that agents eventually learn the true parameter using a randomized gossip scheme. We demonstrate that with high probability the convergence is exponentially fast with a rate dependent on the KL divergence of observations under the true state from observations under the second likeliest state. Furthermore, our work also highlights the possibility of learning under continuous adaptation of network which is a consequence of employing constant, unit stepsize for the algorithm.

关键词： Parameter estimation Network Bayesian learning local information. averaging Dual Fasting Distribute

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Network structure and efficiency of observational social learning

Network structure and efficiency of observational social lea...

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IEEE Conference on Decision and Control

作者： Pooya Molavi Ali Jadbabaie Department of Electrical and Systems Engineering and General Robotics Automation Sensing and Perception (GRASP) Laboratory University of Pennsylvania Philadelphia PA USA

This paper explores the relationship between the structure of a network of agents and how efficiently they can learn a common unknown parameter. Agents repeatedly make private observations which are possibly informative about the unknown parameter; they also communicate their beliefs over the set of conceivable parameter values to their neighbors. It has been shown that for agents to learn the realized state, it is sufficient that they incorporate in their beliefs their private observations in a Bayesian way and the beliefs of their neighbors using a fixed linear rule. In this paper we establish upper and lower bounds on the rate by which agents performing such an update learn the realized state and show that the bounds can be tight. These bounds enable us to compare efficiency of different networks in aggregating dispersed information. Our analysis yields an important insight: for agents in large balanced networks learning is much slower compared to that of a central observer regardless of the distribution of information in the network, whereas unbalanced networks result in near efficient learning if the observations of the centrally positioned agents are much more informative than others' observations.

关键词： Social network services Bayesian methods Upper bound Probability distribution Silicon Observers Nickel

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On Consensus and Exponentially Fast Social Learning

On Consensus and Exponentially Fast Social Learning

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American Control Conference

作者： Pooya Molavi Kamiar Rahnama Rad Alireza Tahbaz-Salehi Ali Jadbabaie Department of Electrical and Systems Engineering and General Robotics Automation Sensing and Perception (GRASP) Laboratory University of Pennsylvania Philadelphia PA 19104-6228 USA Department of Statistics Columbia University New York NY 10027 USA Decision Risk & Operations Division Columbia Business School Columbia University New York NY 10027 USA

ISBN: (纸本)9781457710957

We analyze a model of social learning in which agents desire to identify an unknown state of the world using both their private observations and information they obtain when communicating with agents in their social neighborhood. Every agent holds a belief that represents her opinion on how likely it is for each of several possible states to be the true one. At each time period, agents receive private signals, and also observe the beliefs of their neighbors in a social network. They then update their beliefs by integrating the information available to them in a boundedly rational fashion. We show that in spite of agents' making new private observations perpetually and the myopic and local updating rule employed by them, agents will eventually reach consensus in their beliefs. This is proved by first showing that agents' beliefs over any state whose truth is inconsistent with their collective observations go to zero exponentially fast.

关键词： Bayesian methods Vectors Social network services Probability distribution Convergence Silicon Educational institutions

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A Topological View of Estimation from Noisy Relative Measurements

A Topological View of Estimation from Noisy Relative Measure...

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2011 American Control Conference (ACC)

In this paper we study the problem of estimating the state of sensors in a sensor network from noisy pairwise relative measurements. The underlying sensor network is typically modeled by a graph whose edges correspond to pairwise relative measurements and nodes represent sensors. Using tools from algebraic topology and cohomology theory, we present a new model in which the higher order relations between measurements are captured as simplicial complexes. This allows us to address the fundamental tension between two conflicting goals: finding estimates that are close to obtained measurements, and at the same time are consistent around any sequence of pairwise measurements that form a cycle. By defining a measure of inconsistency around each cycle, we present a one-parameter family of algorithms that solves the estimation problem by identifying and removing the smallest fraction of measurements that make the estimates globally inconsistent. We demonstrate that the inconsistencies are due to topological obstructions and can be decomposed into local and global components that have interesting geometric interpretations. Furthermore, we show that the proposed algorithm is naturally distributed and will provably result in consistent estimates, and more importantly, recovers two sparse estimation algorithms as special cases.

关键词： Sensors Estimation Noise measurement Cost accounting Vectors Distortion measurement Noise

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Aggregate Observational Distinguishability is Necessary and Sufficient for Social Learning

Aggregate Observational Distinguishability is Necessary and ...

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IEEE Conference on Decision and Control

ISBN: (纸本)9781612848006

We study a model of information aggregation and social learning recently proposed by Jadbabaie, Sandroni, and Tahbaz-Salehi, in which individual agents try to learn a correct state of the world by iteratively updating their beliefs using private observations and beliefs of their neighbors. No individual agent's private signal might be informative enough to reveal the unknown state. As a result, agents share their beliefs with others in their social neighborhood to learn from each other. At every time step each agent receives a private signal, and computes a Bayesian posterior as an intermediate belief. The intermediate belief is then averaged with the beliefs of neighbors to form the individual's belief at next time step. We find a set of necessary and sufficient conditions under which agents will learn the unknown state and reach consensus on their beliefs without any assumption on the private signal structure. The key enabler is a result that shows that using this update, agents will eventually forecast the indefinite future correctly.

关键词： Silicon Bayesian methods Social network services Probability distribution Yttrium Computational modeling Vectors

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Variance Analysis of Randomized Consensus in Switching Directed Networks

Variance Analysis of Randomized Consensus in Switching Direc...

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American Control Conference

作者： Victor M. Preciado Alireza Tahbaz-Salehi Ali Jadbabaie Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA 19104 Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA 02139 General Robotics Automation Sensing and Perception (GRASP) Laboratory Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA 19104

ISBN: (纸本)9781424474264

In this paper, we study the asymptotic properties of distributed consensus algorithms over switching directed random networks. More specifically, we focus on consensus algorithms over independent and identically distributed, directed Erdos-Renyi random graphs, where each agent can communicate with any other agent with some exogenously specified probability p. While it is well-known that consensus algorithms over Erdos-Renyi random networks result in an asymptotic agreement over the network, an analytical characterization of the distribution of the asymptotic consensus value remains an open question. In this paper, we provide closed-form expressions for the mean and variance of the asymptotic random consensus value, in terms of the size of the network and the probability of communication p. We also provide numerical simulations that illustrate our results.

关键词： Analysis of variance Algorithm design and analysis Closed-form solution Systems engineering and theory Laboratories robotics and automation Convergence Numerical simulation Computer networks Concurrent computing

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On asymptotic consensus value in directed random networks

On asymptotic consensus value in directed random networks

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IEEE Conference on Decision and Control

作者： Victor M. Preciado Alireza Tahbaz-Salehi Ali Jadbabaie Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA General Robotics Automation Sensing and Perception (GRASP) Laboratory Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA

ISBN: (纸本)9781424477456

We study the asymptotic properties of distributed consensus algorithms over switching directed random networks. More specifically, we focus on consensus algorithms over independent and identically distributed, directed random graphs, where each agent can communicate with any other agent with some exogenously specified probability. While different aspects of consensus algorithms over random switching networks have been widely studied, a complete characterization of the distribution of the asymptotic value for general asymmetric random consensus algorithms remains an open problem. In this paper, we derive closed-form expressions for the mean and an upper bound for the variance of the asymptotic consensus value, when the underlying network evolves according to an i.i.d. directed random graph process.

关键词： Eigenvalues and eigenfunctions Switches Markov processes Silicon Upper bound Closed-form solution Convergence

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Learning Under Social Influence

Learning Under Social Influence

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Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference

作者： Alireza Tahbaz-Salehi Alvaro Sandroni Ali Jadbabaie Department of Economics and Department of Electrical and Systems Engineering University of Pennsylvania Department of Managerial Economics and Decision Sciences Kellogg School of ManagementNorthwestern University the General Robotics AutomationSensing and Perception(GRASP) LaboratoryDepartment of Electrical and Systems EngineeringUniversity of Pennsylvania

In this paper,we study a model of social learning where individuals are under influence of others in their social *** our model,each agent receives private noisy signals about an unobservable,underlying state of the *** the end of each time period,the belief of an individual is equal to the convex combination of her posterior beliefs derived from the signal observed,and the priors of her *** model reduces to the well-known consensus model when private signals are *** show that if the network of social influences is strongly connected,then all agents will have asymptotically correct *** other words,all individuals will be able to asymptotically learn the true state of the world,as far as their observations are ***,we show that all agents assign asymptotically equal beliefs to the true state of the world.

关键词： agent Learning Under Social Influence

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