Iris Tien

Results of Using a Wireless Inertial Measuring System to Quantify Gait Motions in Control Subjects

Gait analysis is important for the diagnosis of many neurological diseases such as Parkinsons. The discovery and interpretation of minor gait abnormalities can aid in early diagnosis. We have used an inertial measuring system mounted on the subjects foot to provide numerical measures of a subjects gait (3-D displacements and rotations), thereby creating an automated tool intended to facilitate diagnosis and enable quantitative prognostication of various neurological disorders in which gait is disturbed. This paper describes the process used for ensuring that these inertial measurement units yield accurate and reliable displacement and rotation data, and for validating the preciseness and robustness of the gait-deconstruction algorithms. It also presents initial results from control subjects, focusing on understanding the data recorded by the shoe-mounted sensor to quantify relevant gait-related motions.

Characterization of gait abnormalities in Parkinson's disease using a wireless inertial sensor system

Gait analysis is important in diagnosing and evaluating certain neurological diseases such as Parkinsons disease (PD). In this paper, we show the ability of our wireless inertial sensor system to characterize gait abnormalities in PD. We obtain physical features of pitch, roll, and yaw rotations of the foot during walking, use principal component analysis (PCA) to select features, and use the support vector machine (SVM) method to create a classification model. In the binary classification task of detecting the presence of PD by distinguishing between PD and control subjects, the model performs with over 93% sensitivity and specificity, and 97.7% precision. Using a cost-sensitive learner to reflect the different costs associated with misclassifying PD and control subjects, performance of 100% specificity and precision is achieved, while maintaining sensitivity of close to 89%. In the multi-class classification task of characterizing parkinsonian gait by distinguishing among PD with significant gait disturbance, PD with no significant gait disturbance, and control subjects, 91.7% class recall for control subjects is achieved and the model performs with 84.6% precision for PD subjects with significant gait disturbance. The features selected for this classification task indicate the features of gait that are principal in discriminating gait abnormalities due to PD compared to a normal gait. These results demonstrate the ability of our wireless inertial sensor system to successfully detect the presence of PD based on physical features of gait and to identify the specific features that characterize parkinsonian gait.

Algorithms for Bayesian network modeling and reliability assessment of infrastructure systems

Novel algorithms are developed to enable the modeling of large, complex infrastructure systems as Bayesian networks (BNs). These include a compression algorithm that significantly reduces the memory storage required to construct the BN model, and an updating algorithm that performs inference on compressed matrices. These algorithms address one of the major obstacles to widespread use of BNs for system reliability assessment, namely the exponentially increasing amount of information that needs to be stored as the number of components in the system increases. The proposed compression and inference algorithms are described and applied to example systems to investigate their performance compared to that of existing algorithms. Orders of magnitude savings in memory storage requirement are demonstrated using the new algorithms, enabling BN modeling and reliability analysis of larger infrastructure systems.

Detection of Damage and Failure Events of Critical Public Infrastructure using Social Sensor Big Data

Public infrastructure systems provide many of the services that are critical to the health, functioning, and security of society. Many of these infrastructures, however, lack continuous physical sensor monitoring to be able to detect failure events or damage that has occurred to these systems. We propose the use of social sensor big data to detect these events. We focus on two main infrastructure systems, transportation and energy, and use data from Twitter streams to detect damage to bridges, highways, gas lines, and power infrastructure. Through a three-step filtering approach and assignment to geographical cells, we are able to filter out noise in this data to produce relevant geolocated tweets identifying failure events. Applying the strategy to real-world data, we demonstrate the ability of our approach to utilize social sensor big data to detect damage and failure events in these critical public infrastructures.

Bayesian Network Methods for Modeling and Reliability Assessment of Infrastructure Systems

Infrastructure systems are essential for a functioning society. These systems, however, are aging and subject to hazards of increasing frequency and severity. This chapter presents novel Bayesian network (BN) methodologies to model and assess the reliability of complex infrastructure systems. BNs are particularly well suited to the analysis of civil infrastructures, where information about the systems is often uncertain and evolving in time. In this environment, BNs handle information probabilistically to support engineering decision making under uncertainty, and are capable of updating to account for new information as it becomes available. This chapter addresses one of the major limitations of the BN framework in analyzing infrastructure systems, namely the exponentially increasing memory storage required as the size and complexity of the system increases. Traditionally, this has limited the size of the systems that can be tractably modeled as BNs. Novel compression and inference algorithms are presented to address this memory storage challenge. These are combined with several heuristics to improve the computational efficiency of the algorithms. Through the application of these algorithms and heuristics to example systems, the proposed methodologies are shown to achieve significant gains in both memory storage and computation time. Together, these algorithms enable larger infrastructure systems to be modeled as BNs for system reliability analysis.

Reliability Assessment of Critical Infrastructure Using Bayesian Networks

AbstractThe authors present a Bayesian network (BN)-based approach for modeling and reliability assessment of infrastructure systems. The BN is a powerful framework that is able to account for unce...

Probabilistic multi-scale modeling of interdependencies between critical infrastructure systems for resilience

Abstract The prevalence of aging infrastructure and an increase in cascading failures have highlighted the need to focus on building strong, interdependent infrastructure systems to increase resilience. To understand the ways infrastructure systems depend on one another, we define three comprehensive interdependency types – service provision, geographic, and access for repair. We propose a methodology to model interdependencies probabilistically using a novel Bayesian network approach. By understanding how these interdependencies affect the fragility of overall systems, infrastructure owners can work towards creating more resilient infrastructure systems that sustain less damage from natural hazards and targeted attacks, and restore services to communities rapidly. Generalized expressions to create the multi-scale Bayesian network model accounting for each interdependency type are presented and applied to a real interdependent water, power, and gas network to demonstrate their use. These models enable us to probabilistically infer which interdependencies have the most critical effects and prioritize components for repair or reinforcement to increase resilience.

Framework for Probabilistic Assessment of Maximum Nonlinear Structural Response Based on Sensor Measurements: Discretization and Estimation

AbstractA probabilistic framework to draw real-time inferences on the maximum response of an uncertain nonlinear structural system under stochastic excitation based on sensor measurements is propos...

Algorithms for Bayesian Network Modeling, Inference, and Reliability Assessment for Multistate Flow Networks

AbstractThe Bayesian network (BN) is a useful tool for the modeling and reliability assessment of civil infrastructure systems. For a system comprising many interconnected components, it captures t...

Impacts of Climate Change on the Assessment of Long-Term Structural Reliability

AbstractGlobal climate change has triggered studies across various science and engineering fields. This study demonstrates the need to account for climate change in assessing structural reliability...