Transportation System Performance Measures Using Internet of Things Data

The transportation system is undergoing a rapid change with innovative and promising technologies that provide real-time data for a variety of applications. As we transition into a technology-driven era and Internet of Things (IoT) applications, where everything is connected via a network of smart sensors and cloud computing, there will be an increasing amount of real-time data that will allow a better understanding of the transportation system. Devices emerging as a part of this connected environment can provide new and valuable data sources in a variety of transportation areas including safety, mobility, operations and intelligent transportation systems. Agencies and transportation professionals require effective performance measures and visualization tools to mine this big data to make design, operation, maintenance and investment decisions to improve the overall system performance. This dissertation discusses the development and demonstration of performance measures that leverage data from these emerging IoT devices to support analysis and guide investment decisions. Selected case studies are presented that demonstrate the impact of these new data sources on design, operation, and maintenance decisions. Performance measures such as vibration, noise levels and retroreflectivity were used to conduct a comprehensive assessment of different rumble strip configurations in the roadway and aviation environment. The results indicated that the 12 in sinusoidal wavelength satisfied the National Cooperative Highway Research Program (NCHRP) recommendations and reduced the noise exposure to adjacent homeowners. The application of low-cost rumble strips to mitigate runway incursions at general aviation airports was evaluated using the accelerations on the airframe. Although aircraft are designed for significant g-forces on landing, the results of analyzing accelerometers installed on airframes showed that long-term deployment of rumble strips is a concern for aircraft manufacturers as repeated traversal on the rumble strips may lead to excessive airframe fatigue. A suite of web dashboards and performance measures were developed to evaluate the impact of signal upgrades, signal retiming and maintenance activities on 138 arterials in the Commonwealth of Pennsylvania. For five corridors analyzed before and after an upgrade, the study found a reduction of 1.2 million veh-hours of delay, 10,000 tons of CO2 and an economic benefit of $32 million. Several billion dollars per year is expended upon security checkpoint screening at airports. Using wait time data from consumer electronic devices over a one-year period, performance dashboards identified periods of the day with high median wait times. The performance measures outlined in this study provided scalable techniques to analyze operating irregularities and identify opportunities for improving service. Reliability and median wait times were also used as performance measures to compare the standard and expedited security screening. The results found that the expedited screening was highly reliable than the standard screening and had a median wait time savings of 5.5 minutes. Bike sharing programs are an eco-friendly mode of transportation gaining immense popularity all over the world. Several performance measures are discussed which analyze the usage patterns, user behaviors and effect of weather on a bike sharing program initiated at Purdue University. Of the 1626 registered users, nearly 20% of them had at least one rental and around 6% had more than 100 rentals, with four of them being greater than 500 rentals. Bikes were rented at all hours of the day, but usage peaked between 11:00 and 19:00 on average. On a yearly basis, the rentals peaked in the fall semester, especially during September, but fell off in October and November with colder weather. Preliminary results from the study also identified some operating anomalies, which allowed the stakeholders to implement appropriate policy revisions. There are a number of outlier filtering algorithms proposed in the literature, however, their performance has never been evaluated. A curated travel time dataset was developed from real-world data, and consisted of 31,621 data points with 243 confirmed outliers. This dataset was used to evaluate the efficiency of three common outlier filtering algorithms, median absolute deviation, modified z-score and, box and whisker plots. The modified Z-score had the best performance with successful removal of 70% of the confirmed outliers and incorrect removal of only 5% of the true samples. The accuracy of vehicle to infrastructure (V2I) communication is an important metric for connected vehicle applications. Traffic signal state indication is an early development in the V2I communication that allows connected vehicles to display the current traffic signal status on the driver dashboard as the vehicle approaches an intersection. The study evaluated the accuracy of this prediction with on-field data and results showed a degraded performance during phase omits and force-offs. Performance measures such as, the probability of expected phase splits and the probability of expected green for a phase, are discussed to enhance the accuracy of the prediction algorithm. These measures account for the stochastic variations due to detectors actuations and will allow manufacturers and vendors to improve their algorithm. The application of these performance measures across three transportation modes and the transportation focus areas of safety, mobility and operations will provide a framework for agencies and transportation professionals to assess the performance of system components and support investment decisions

Intelligent computing applications to assist perceptual training in medical imaging

The research presented in this thesis represents a body of work which addresses issues in medical imaging, primarily as it applies to breast cancer screening and laparoscopic surgery. The concern here is how computer based methods can aid medical practitioners in these tasks. Thus, research is presented which develops both new techniques of analysing radiologists performance data and also new approaches of examining surgeons visual behaviour when they are undertaking laparoscopic training. Initially a new chest X-Ray self-assessment application is described which has been developed to assess and improve radiologists performance in detecting lung cancer. Then, in breast cancer screening, a method of identifying potential poor performance outliers at an early stage in a national self-assessment scheme is demonstrated. Additionally, a method is presented to optimize whether a radiologist, in using this scheme, has correctly localised and identified an abnormality or made an error. One issue in appropriately measuring radiological performance in breast screening is that both the size of clinical monitors used and the difficulty in linking the medical image to the observer s line of sight hinders suitable eye tracking. Consequently, a new method is presented which links these two items. Laparoscopic surgeons have similar issues to radiologists in interpreting a medical display but with the added complications of hand-eye co-ordination. Work is presented which examines whether visual search feedback of surgeons operations can be useful training aids

A Novel Real-Time Non-invasive Hemoglobin Level Detection Using Video Images from Smartphone Camera

Hemoglobin level detection is necessary for evaluating health condition in the human. In the laboratory setting, it is detected by shining light through a small volume of blood and using a colorimetric electronic particle counting algorithm. This invasive process requires time, blood specimens, laboratory equipment, and facilities. There are also many studies on non-invasive hemoglobin level detection. Existing solutions are expensive and require buying additional devices. In this paper, we present a smartphone-based non-invasive hemoglobin detection method. It uses the video images collected from the fingertip of a person. We hypothesized that there is a significant relation between the fingertip mini-video images and the hemoglobin level by laboratory gold standard. We also discussed other non-invasive methods and compared with our model. Finally, we described our findings and discussed future works

Modelling optimal use of tests for monitoring disease progression and recurrence

Background: Monitoring to identify disease recurrence or progression is common, often with limited evidence to support the tests used, subsequent decisions, frequency and duration of monitoring. Aims: To develop methods for designing evidence-based monitoring strategies and estimating measurement error, a key consideration in selecting monitoring tests. Methods: To investigate studies of measurement error: frameworks were identified; design, analysis and reporting of studies were reviewed; a case study was analysed; and, simulation studies were performed to evaluate varying sample size and outlier detection methods. To develop methods for designing monitoring strategies the methods literature was reviewed and simulation models were developed and validated. Results: Biological variability studies are often poorly designed and reported. Studies are frequently small and may not produce valid results; the required precision of estimates can inform the sample size. Outlier detection can negatively bias variability estimates; methods should be used with caution, with interpretation allowing for potential bias. Modelling monitoring data requires knowledge of the natural history of disease, test performance and measurement error; such evaluation enables selection of evidence-based monitoring strategies prior to full-scale investigation. Conclusions: Poor monitoring tests can be identified early using small-scale studies and monitoring strategies should be optimised prior to full evaluation

Glaucoma Referral Refinement in Ireland: Managing the Sensitivity-Specificity Paradox in Optometric Practice

Purpose: Glaucoma referral refinement (GRR) has pr ven a successful demand management strategy for glaucoma suspect cases in the United Kingdom (UK). A GRR clinic was established in Dublin, Ireland to investigate the clinical viability of this pathway outside the UK\u27s National Health Service (NHS) structures, and away from the influence of National Institute for Clinical Excellence (NICE) guidance

An Open-Source Platform for Real-Time Preliminary Diagnosis amongst Adults using Data Analytics

Depression can be defined as a mental health disorder characterized by persistently depressed mood, loss of interest in activities, causing significant impairment in daily life.  Technical intervention to screen depression in non-clinical population which records, classify depression on the basis of severity and provide features or predictors that discriminate the classification of depression among non-clinical population comprising of college students is the main area of the study. Beck Depression Inventory – II (BDI-II), as per Diagnostic and Statistical manual of Mental disorder (DSM IV) is used to screen depression and its severity. Indicators are determined on the basis of how well the features or predictors can discriminate the classes of depression severity.  Providing quality indicators which help in supporting the process can be considered as symptoms for screening depression.  Descriptive analytics is used in order to find the underlying pattern of the responses captured, factor analysis groups variables on the basis of correlation between patterns of the responses to reduce dimension.  The approach for supervised descriptive analysis method that takes BDI-II questions as features and refine the features using information gain and linear discriminant analysis as feature selection algorithm. The classification of severity of depression is done using Support vector machine (SVM).

Characterizing the Information Needs of Rural Healthcare Practitioners with Language Agnostic Automated Text Analysis

Objectives – Previous research has characterized urban healthcare providers\u27 information needs, using various qualitative methods. However, little is known about the needs of rural primary care practitioners in Brazil. Communication exchanged during tele-consultations presents a unique data source for the study of these information needs. In this study, I characterize rural healthcare providers\u27 information needs expressed electronically, using automated methods. Methods – I applied automated methods to categorize messages obtained from the telehealth system from two regions in Brazil. A subset of these messages, annotated with top-level categories in the DeCS terminology (the regional equivalent of MeSH), was used to train text categorization models, which were then applied to a larger, unannotated data set. On account of their more granular nature, I focused on answers provided to the queries sent by rural healthcare providers. I studied these answers, as surrogates for the information needs they met. Message representations were generated using methods of distributional semantics, permitting the application of k-Nearest Neighbor classification for category assignment. The resulting category assignments were analyzed to determine differences across regions, and healthcare providers. Results – Analysis of the assigned categories revealed differences in information needs across regions, corresponding to known differences in the distributions of diseases and tele-consultant expertise across these regions. Furthermore, information needs of rural nurses were observed to be different from those documented in qualitative studies of their urban counterparts, and the distribution of expressed information needs categories differed across types of providers (e.g. nurses vs. physicians). Discussion – The automated analysis of large amounts of digitally-captured tele-consultation data suggests that rural healthcare providers\u27 information needs in Brazil are different than those of their urban counterparts in developed countries. The observed disparities in information needs correspond to known differences in the distribution of illness and expertise in these regions, supporting the applicability of my methods in this context. In addition, these methods have the potential to mediate near real-time monitoring of information needs, without imposing a direct burden upon healthcare providers. Potential applications include automated delivery of needed information at the point of care, needs-based deployment of tele-consultation resources and syndromic surveillance. Conclusion – I used automated text categorization methods to assess the information needs expressed at the point of care in rural Brazil. My findings reveal differences in information needs across regions, and across practitioner types, demonstrating the utility of these methods and data as a means to characterize information needs

Vital Sensory Kit For Use With Telemedicine In Developing Countries

In many developing countries, a large percentage of the population lacks access to adequate healthcare. This is especially true in India where close to 70% of the population lives in rural areas and has little to no access to hospitals or clinics. People living in rural India often times cannot afford to pay to see a doctor should they need to make the journey to a hospital. Telemedicine, a breakthrough in the past couple decades, has broken down the barrier between the patient and the physician. It has slowly been implemented in India to make doctors more available to patients through the use of video conferences and other forms of communication. A compact and affordable kit has been developed that will be used to take a patient’s blood pressure, heart rate, blood glucose concentration and oxygen saturation. Our most novel contribution is the non-invasive glucose sensor that will use a near-infrared LED and photodiode in the patient’s earlobe. Currently millions of diabetics do this by pricking their finger. By wirelessly sending data results from the vital sign kit, the first essential part of a treatment can be carried out via wireless communication, saving the doctor and patient time and money

Statistical methods for automated drug susceptibility testing: Bayesian minimum inhibitory concentration prediction from growth curves

Determination of the minimum inhibitory concentration (MIC) of a drug that prevents microbial growth is an important step for managing patients with infections. In this paper we present a novel probabilistic approach that accurately estimates MICs based on a panel of multiple curves reflecting features of bacterial growth. We develop a probabilistic model for determining whether a given dilution of an antimicrobial agent is the MIC given features of the growth curves over time. Because of the potentially large collection of features, we utilize Bayesian model selection to narrow the collection of predictors to the most important variables. In addition to point estimates of MICs, we are able to provide posterior probabilities that each dilution is the MIC based on the observed growth curves. The methods are easily automated and have been incorporated into the Becton--Dickinson PHOENIX automated susceptibility system that rapidly and accurately classifies the resistance of a large number of microorganisms in clinical samples. Over seventy-five studies to date have shown this new method provides improved estimation of MICs over existing approaches.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS217 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org