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Pujitha Gunaratne

Pujitha Gunaratne
Toyota Motor North America

Doctor of Engineering

About

36
Publications
3,978
Reads
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160
Citations

Publications

Publications (36)
Article
Full-text available
Background Rapid and irregular ventricular rates (RVR) are an important consequence of atrial fibrillation (AF). Raw accelerometry data in combination with electrocardiogram (ECG) data have the potential to distinguish inappropriate from appropriate tachycardia in AF. This can allow for the development of a just-in-time intervention for clinical tr...
Preprint
Understanding occupant-vehicle interactions by modeling control transitions is important to ensure safe approaches to passenger vehicle automation. Models which contain contextual, semantically meaningful representations of driver states can be used to determine the appropriate timing and conditions for transfer of control between driver and vehicl...
Preprint
Full-text available
With increasing automation in passenger vehicles, the study of safe and smooth occupant-vehicle interaction and control transitions is key. In this study, we focus on the development of contextual, semantically meaningful representations of the driver state, which can then be used to determine the appropriate timing and conditions for transfer of c...
Article
Full-text available
This paper introduces a novel method for classifying and predicting cardiac arrhythmia events via a special type of deterministic probabilistic finite-state automata (DPFA). The proposed method constructs the underlying state space and transition probabilities of the DPFA model directly from the input data. The algorithm was employed in the predict...
Article
Objective Understanding the factors that affect drivers’ response time in takeover from automation can help guide the design of vehicle systems to aid drivers. Higher quantiles of the response time distribution might indicate a higher risk of an unsuccessful takeover. Therefore, assessments of these systems should consider upper quantiles rather th...
Article
Objective: Our goal is to measure real-world effects of at-risk driver physiology on safety-critical tasks like driving by monitoring driver behavior and physiology in real-time. Drivers with type 1 diabetes (T1D) have an elevated crash risk that is linked to abnormal blood glucose, particularly hypoglycemia. We tested the hypotheses that (1) T1D d...
Conference Paper
Full-text available
The advent of portable cardiac monitoring devices has enabled real-time analysis of cardiac signals. These devices can be used to develop algorithms for real-time detection of dangerous heart rhythms such as ventricular arrhythmias. This paper presents a Markov model based algorithm for real-time detection of ventricular tachycardia, ventricular fl...
Conference Paper
Full-text available
Drivers with diabetes have significantly increased risk for vehicle crashes, raising an important issue of patient and public safety. For drivers with insulin-dependent type 1 diabetes (T1D), poor glucose control and hypoglycemia can impair the cognitive abilities (e.g., attention, memory, and decision-making) required for safe performance in compl...
Article
Cardiovascular diseases are highly prevalent and fatal medical conditions cause many adult deaths annually. The occurrence of a debilitating cardiac condition while driving could suddenly render a driver unable to safely operate a vehicle, including bringing a vehicle to a stop in order to request medical assistance. Currently, many people are at r...
Article
Vehicles with SAE Level 2 or 3 automation rely on the driver to intervene and resume control when failures occur. In cases which the driver must steer upon regaining control, the initial conditions of the vehicle’s state variables can affect the success of the drivers' recovery. Hence, a model to determine the consequences of these initial states c...
Article
Drivers’ steering adjustments can be categorized into one-time and chain corrections. One-time corrections lead to no further steering corrections for a minimum of one second, while chain corrections have at least two consecutive steering actions. Chain corrections represent a novel indicator of steering instability. Evolving vehicle dynamics along...
Conference Paper
Atrial Fibrillation (AFib) is by itself a strong risk factor for many life-threatening heart diseases. An estimated 2.7 to 6.1 million people in the United States have AFib. With the aging of the U.S. population, this number is expected to increase. In this preliminary study, a heart rate-duration criteria region is proposed to automatically label...
Article
Our goal is to address the need for driver-state detection using wearable and in-vehicle sensor measurements of driver physiology and health. To address this goal, we deployed in-vehicle systems, wearable sensors, and procedures capable of quantifying real-world driving behavior and performance in at-risk drivers with insulin-dependent type 1 diabe...
Article
Recent progress in autonomous and semi-autonomous driving has been made possible in part through an assortment of sensors that provide the intelligent agent with an enhanced perception of its surroundings. It has been clear for quite some while now that for intelligent vehicles to function effectively in all situations and conditions, a fusion of d...
Conference Paper
Full-text available
Our goal is to address the need for driver-state detection using wearable and in-vehicle sensor measurements of driver physiology and health. To address this goal, we deployed in-vehicle systems, wearable sensors, and procedures capable of quantifying real-world driving behavior and performance in at-risk drivers with insulin-dependent type 1 diabe...
Conference Paper
Full-text available
This pilot study tackles the overarching need for driver-state detection through real-world measurements of driver behavior and physiology in at-risk drivers with type 1 diabetes mellitus (DM). 35 drivers (19 DM, 14 comparison) participated. Real-time glucose levels were measured over four weeks with continuous glucose monitor (CGM) wearable sensor...
Conference Paper
Full-text available
We compared the eye movements of novice drivers and experienced drivers while they drove a simulated driving scenario that included a number of intersections interspersed with stretches of straight road. The intersections included non-hazard events. Cassavaugh, Bos, McDonald, Gunaratne, & Backs (2013) attempted to model attention allocation of expe...
Conference Paper
Analysis of naturalistic driving data provides a rich set of semantics which can be used to determine the driving characteristics that could lead to crashes and near-crashes. In this paper, we introduce “drive quality” analysis as part of the drive analysis process of naturalistic driving studies (NDSs) that we have previously introduced in [1]. In...
Conference Paper
Naturalistic driving studies (NDS) provide critical information about driving behaviors and characteristics that could lead to crashes and near-crashes. Such studies involve analysis of large volumes of data from multiple sensors and detection and extraction of critical events is an important step in NDS. This paper introduces techniques that analy...
Conference Paper
Naturalistic driving studies (NDS) capture huge amounts of drive data, that is analyzed for critical information about driver behavior, driving characteristics etc. Moreover, NDS involve data collected from a wide range of sensing technologies in cars and this makes the analysis of this data a challenging task. In this paper, we propose a multimoda...
Patent
Full-text available
The present invention includes a method of detecting drowsy facial expressions of vehicle drivers under changing illumination conditions. The method includes capturing an image of a person's face using an image sensor, detecting a face region of the image using a pattern classification algorithm, and performing, using an active appearance model alg...
Conference Paper
The area of human machine interaction has been immersed into transportation research for many years and has embraced in intelligent transportation systems for the development of next-generation active safety systems in recent years. It has long been identified that the driver distraction plays a major role in traffic accidents and application of im...
Article
To estimate asymmetry in normal and pathological facial functions using an established computer generated objective evaluation technique. Analysis was performed on three-dimensional (3-D) data captured using a specially designed 3-D face shape measurement system. Six healthy volunteers and six patients with Bell's palsy were analyzed for forced eye...
Conference Paper
An objective evaluation method to analyze the degree of motion dysfunction in facial expressions due to paralysis is presented. The analysis are based on range data captured for pre-determined facial actions of patients with facial paralysis. The dysfunctions in expressions are analyzed by estimating the degree of asymmetry between left and right s...
Conference Paper
Construction of human like avatars is a key to produce realistic animation in virtual reality environments and has been a commonplace in present day applications. However most of the models proposed to date intuitively assume human face as a symmetric entity. Such assumptions produce unfavorable drawbacks in applications where the analysis and esti...
Article
We present a simple but effective approach to estimate the asymmetry in facial expressions using range data. A face measuring system called "Cubicfacer" that equips two laser scanners and a color CCD camera measures the human faces and produces range and color texture images simultaneously. Five pre-determined facial actions are measured on each su...
Conference Paper
A method of constructing a 3D model of human face by integrating multiple range images that were measured at different view directions is presented. The images were obtained by a high-speed rangefinder system which is capable of measuring the frontal face and capturing a texture image in approximately one second. A complete face model construction...
Conference Paper
In this paper we present an extended geometric ap- proach, that detects and eliminate the specular points in a texture image of shiny objects, with the help of corre- sponding range data. The approach neither count on spectral variations of the texture image nor employs rigid constrains on illumination sources, such as point light source limitation...

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Projects

Projects (3)
Archived project
This project addressed the need for in-vehicle driver-state detection using wearable and in-vehicle measurements of driver physiology and health. We deployed a multidisciplinary approach to quantify the relationship between driver physiology and on-road safety behavior in drivers with insulin-dependent diabetes mellitus (DM). Using this approach, we successfully demonstrated the feasibility and utility of procedures capable of quantifying real-world driving behavior to determine the level and patterns of glucose control needed to produce meaningful improvements in driver safety in drivers with DM. Our experimental platform included detailed, continuous measurements of naturalistic driving, glucose levels, and activity patterns in driver, with an without DM, from in-vehicle Black Box sensor instrumentation, continuous glucose monitors (CGMs), and wearable activity sensors. This project demonstrates a new experimental platform and data relevant to improving safety and mobility in drivers with DM using a variety of in-vehicle and physiologic sensors to record real-world driver behavior.
Project
The goal of this novel project is to detect and predict on-road risk from wearable sensor measurements of driver physiology, health, and behavior. To achieve this goal, we are studying individuals with diabetes (type 1 and 2) and linking dynamic patterns of driving behavior and risk to an individual driver's physiology (glucose levels and heart rate), cognition, and health (sleep dysfunction and obesity). The results of this projects will advance the development of gold standards methods and supportive in-vehicle technology, like advanced driver-assistance systems (ADAS), for safety-critical driver-state detection and prediction.