Edgar Lobaton

North Carolina State University | NCSU · Department of Electrical and Computer Engineering

Knee osteoarthritis is a major cause of global disability and is a major cost for the healthcare system. Lower extremity loading is a determinant of knee osteoarthritis onset and progression; however, technology that assists rehabilitative clinicians in optimizing key metrics of lower extremity loading is significantly limited. The peak vertical co...

Cough is an important defense mechanism of the respiratory system and is also a symptom of lung diseases, such as asthma. Acoustic cough detection collected by portable recording devices is a convenient way to track potential condition worsening for patients who have asthma. However, the data used in building current cough detection models are ofte...

p>Cough is an important defense mechanism of the respiratory system and is also a symptom of lung diseases, such as asthma. Acoustic cough detection collected by portable recording devices is a convenient way to track potential condition worsening for patients who have asthma. However, the data used in building current cough detection models are of...

p>Cough is an important defense mechanism of the respiratory system and is also a symptom of lung diseases, such as asthma. Acoustic cough detection collected by portable recording devices is a convenient way to track potential condition worsening for patients who have asthma. However, the data used in building current cough detection models are of...

Physical inspection and sorting of foraminifera is a necessity in many research labs, as foraminifera serve as paleoenvironmental and chronostratigraphic indicators. In order to gain counts of species from samples, analyze chemical compositions, or extract morphological properties of foraminifera, research labs require human time and effort handlin...

With the rise of the Internet of Things, strategies for effectively processing big data are essential for discovering meaningul insights. The time series datasets produced by groups of interconnected devices contain valuable underlying patterns. Recent works have extracted patterns from spatio-temporal datasets to aid in road network generation, ac...

Body-rocking is an undesired stereotypical motor movement performed by some individuals, and its detection is essential for self-awareness and habit change. We envision a pipeline that includes inertial wearable sensors and a real-time detection system for notifying the user so that they are aware of their body-rocking behavior. For this task, simi...

Aging is associated with impairment in postural control in humans. While dogs are a powerful model for the study of aging, the associations between age and postural control in this species have not yet been elucidated. The aims of this work were to establish a reliable protocol to measure center of pressure excursions in standing dogs and to determ...

This chapter surveys the state-of-the-art related to the building blocks of wearable cyberphysical systems for health monitoring and highlights its potential to revolutionize healthcare, specifically chronic disease management. The common sensing modalities and their corresponding wearable form factors are summarized for the application areas of ca...

Aging is associated with changes in the sensory-motor system that could lead to dynamic instability. In fact, postural control deficits have been proposed as an early indicator of frailty. Measurements of the displacement of the center of pressure (COP) using pressure mat data are useful tools to determine postural steadiness. Companion dogs repres...

Aging is associated with changes in the sensory-motor system that could lead to dynamic instability. In fact, postural control deficit has been proposed as an early indicator of frailty. Measurements of the displacement of the center of pressure (COP) using pressure mats are useful tools to determine postural steadiness. Companion dogs represent a...

Longitudinal fetal health monitoring is essential for high-risk pregnancies. Heart rate and heart rate variability are prime indicators of fetal health. In this work, we implemented two neural network architectures for heartbeat detection on a set of fetal phonocardiogram signals captured using fetal Doppler and a digital stethoscope. We test the e...

Several recent research efforts have shown that the bioelectrical stimulation of their neuro-mechanical system can control the locomotion of Madagascar hissing cockroaches (Gromphadorhina portentosa). This has opened the possibility of using these insects to explore centimeter-scale environments, such as rubble piles in urban disaster areas. We pre...

Cough detection can provide an important marker to monitor chronic respiratory conditions. However, manual techniques which require human expertise to count coughs are both expensive and time-consuming. Recent Automatic Cough Detection Algorithms (ACDAs) have shown promise to meet clinical monitoring requirements, but only in recent years they have...

Preprint of manuscript submitted to an IEEE Journal currently under revision.

Preprint of manuscript submitted to an IEEE Journal currently under revision.

Environmental context prediction is important for wearable robotic applications, such as terrain-adaptive control. System efficiency is critical for wearable robots, in which system resources (e.g., processors and memory) are highly constrained. This article aims to address the system efficiency of real-time environmental context prediction for low...

For many horticultural crops, variation in quality (e.g., shape and size) contributes significantly to the crop’s market value. Metrics characterizing less subjective harvest quantities (e.g., yield and total biomass) are routinely monitored. In contrast, metrics quantifying more subjective crop quality characteristics such as ideal size and shape...

Disaster robotics is a growing field that is concerned with the design and development of robots for disaster response and disaster recovery. These robots assist first responders by performing tasks that are impractical or impossible for humans. Unfortunately, current disaster robots usually lack the maneuverability to efficiently traverse these ar...

For many horticultural crops, variation in quality (e.g., shape and size) contribute significantly to the crop's market value. Metrics characterizing less subjective harvest quantities (e.g., yield and total biomass) are routinely monitored. In contrast, metrics quantifying more subjective crop quality characteristics such as ideal size and shape r...

Lower limb prosthesis can benefit from embedded systems capable of applying computer vision techniques to enhance autonomous control and context awareness for intelligent decision making. In order to fill in the gap of current literature of autonomous systems for prosthetic legs employing computer vision methods, we evaluate the performance capabil...

Computer vision has shown promising potential in wearable robotics applications (e.g., human grasping target prediction and context understanding). However, in practice, the performance of computer vision algorithms is challenged by insufficient or biased training, observation noise, cluttered background, etc. By leveraging Bayesian deep learning (...

The performance of a low-power single-lead armband in generating electrocardiogram (ECG) signals from the chest and left arm was validated against a BIOPAC MP160 benchtop system in real-time. The filtering performance of three adaptive filtering algorithms, namely least mean squares (LMS), recursive least squares (RLS), and extended kernel RLS (EKR...

Reliable environmental context prediction is critical for wearable robots (e.g., prostheses and exoskeletons) to assist terrain-adaptive locomotion. This article proposed a novel vision-based context prediction framework for lower limb prostheses to simultaneously predict human's environmental context for multiple forecast windows. By leveraging th...

There have been recent efforts to increase the degree of automation and frequency of data collection for construction applications using Unmanned Aerial/Ground Vehicles (UAV/UGV). However, the current practice of data collection is traditionally performed, which is manual, costly, time-consuming, and error-prone. Developing vision-based mobile robo...

Physiological responses are essential for health monitoring. Wearable devices are providing greater populations of people with the ability to monitor physiological signals during their day to day activities. However, wearable devices are particularly susceptible to degradation of signal quality due to noise from motion artifacts, environment, and u...

Wearable sensors have been shown to be effective for promoting self-awareness, wellness and re-education. In this work, we perform a preliminary study analyzing the real-time detection and annotation of body-rocking behavior in individuals, which is a type of Stereotypical Motor Movement (SMM). We develop a platform for real-time annotation and det...

This paper aims to investigate the visual strategy of transtibial amputees while they are approaching the transition between level-ground and stairs and compare it with that of able-bodied individuals. To this end, we conducted a pilot study where two transtibial amputee subjects and two able-bodied subjects transitioned from level-ground to stairs...

Camera-equipped unmanned vehicles (UVs) have received a lot of attention in data collection for construction monitoring applications. To develop an autonomous platform, the UV should be able to process multiple modules on an embedded platform. Pixel-wise semantic segmentation provides a UV with the ability to be contextually aware of its surroundin...

The use of camera-equipped robotic platforms for data collection and visually monitoring applications is exponentially growing. Cluttered construction sites with many objects on the ground are challenging environments for a mobile unmanned ground vehicle (UGV) to navigate. This study presents a mobile UGV equipped with a stereo camera and a robotic...

An autonomous robot that can monitor a construction site should be able to be can contextually detect its surrounding environment by recognizing objects and making decisions based on its observation. Pixel-wise semantic segmentation in real-time is vital to building an autonomous and mobile robot. However, the learning models’ size and high memory...

Camera-equipped unmanned vehicles (UVs) have received a lot of attention in data collection for construction monitoring applications. To develop an autonomous platform, the UV should be able to process multiple modules (e.g., context-awareness, control, localization, and mapping) on an embedded platform. Pixel-wise semantic segmentation provides a...

Over the past few years, the use of camera-equipped robotic platforms for data collection and visually monitoring applications has exponentially grown. Cluttered construction sites with many objects (e.g., bricks, pipes, etc.) on the ground are challenging environments for a mobile unmanned ground vehicle (UGV) to navigate. To address this issue, t...

Wearable health monitoring has emerged as a promising solution to the growing need for remote health assessment and growing demand for personalized preventative care and wellness management. Vital signs can be monitored and alerts can be made when anomalies are detected, potentially improving patient outcomes. One major challenge for the use of wea...

To increase the degree of automation and frequency of data collection for monitoring construction sites, there has been a rapid increase in the number of studies, in the past few years, that developed and/or examined mobile robotic applications in construction. These vision-based platforms capable of autonomous navigation and scene understanding ar...

One of the major challenges of a real-time autonomous robotic system for construction monitoring is to simultaneously localize, map, and navigate over the lifetime of the robot, with little or no human intervention. Past research on Simultaneous Localization and Mapping (SLAM) and context-awareness are two active research areas in the computer visi...

Lower-limb robotic prosthetics can benefit from context awareness to provide comfort and safety to the amputee. In this work, we developed a terrain identification and surface inclination estimation system for a prosthetic leg using visual and inertial sensors. We built a dataset from which images with high sharpness are selected using the IMU sign...

Physiological responses are essential for health monitoring. However, modeling the complex interactions be- tween them across activity and environmental factors can be challenging. In this paper, we introduce a framework that identifies the state of an individual based on their activity, trains predictive models for their physiological response wit...

The goal of this study is to characterize the accuracy of prediction of physiological responses for varying forecast lengths using multi-modal data streams from wearable health monitoring platforms. We specifically focus on predicting breathing rate due to its significance in medical and exercise physiology research. We implement a nonlinear suppor...

Prediction of physiological responses can have a number of applications in the health and medical fields. However, this can be a challenging task due to interdependencies between these responses, physical activities, environmental factors and the individual’s mental state. In this work, we focus on forecasting physiological responses in dynamic sce...

This paper proposes a novel framework for activity recognition from 3D motion capture data using topological data analysis (TDA). We extract point clouds describing the oscillatory patterns of body joints from the principal components of their time series using Taken's delay embedding. Topological persistence from TDA is exploited to extract topolo...

In this paper we propose a hierarchical activity clustering methodology which incorporates the use of topological persistence analysis. Our clustering methodology captures the hierarchies present in the data and is therefore able to show the dependencies that exist between these activities. We make use of an aggregate persistence diagram to select...

We present an approach for global exploration and mapping of unknown environments using a swarm of cyborg insects, known as biobots, for emergency response scenarios under minimal sensing and localization constraints. We exploit natural stochastic motion models and controlled locomotion of biobots in conjunction with an aerial leader to explore and...

Biobotic research involving neurostimulation of instrumented insects to control their locomotion is finding potential as an alternative solution towards development of centimeter-scale distributed swarm robotics. To improve the reliability of biobotic agents, their control mechanism needs to be precisely characterized. To achieve this goal, this pa...

This paper explores the idea of identifying activities from muscle activation which is captured by wearable ECG recording devices that use wet and textile electrodes. Most of the devices available today filter out the high frequency components to retain only the signal related to an ECG. We explain how the high frequency components that correspond...

In this study, we present and analyze a framework for geometric and topological estimation for mapping of unknown environments. We consider agents mimicking motion behaviors of cyborg insects, known as biobots, and exploit coordinate-free local interactions among them to infer geometric and topological information about the environment, under minim...

Utilizing the latest neural engineering developments, researchers have enabled biobotic insects that function as search-and-rescue agents to help map under-rubble environments and locate survivors and hazardous conditions. The Web extra at http://youtu.be/oJXEPcv-FMw is a video in which authors Alper Bozkurt, Edgar Lobaton, and Mihail Sichitiu demo...

A cyber-physically organized swarm of insect biobots or biological robots can aid first responders in search-and-rescue scenarios after natural disasters or earthquakes by establishing an under-rubble sensor network. In such a network, the nodes are represented by the insect biobots equipped with electronic backpacks utilizing a system-on-chip. Thi...

In this paper, we present a motion segmentation based robust multi-target tracking technique for on-road obstacles. Our approach uses depth imaging information, and integrates persistence topology for segmentation and min-max network flow for tracking. To reduce time as well as computational complexity, the max flow problem is solved using a dynami...

In this paper, we present an approach for dynamic exploration and mapping of unknown environments using a swarm of biobotic sensing agents, with a stochastic natural motion model and a leading agent (e.g., an unmanned aerial vehicle). The proposed robust mapping technique constructs a topological map of the environment using only encounter informat...

In this demonstration, we present a topological mapping system to be used with biobotic insects in order to sketch maps of unknown arenas using only neighbor to neighbor interactions among the agents. Biobotic insects fuse the locomotory advantages of insects with wireless sensing technology in form of electronic backpacks to function as search and...

Image registration aims to identify the mapping between corresponding locations in an anatomic structure. Most traditional approaches solve this problem by minimizing some error metric. However, they do not quantify the uncertainty behind their estimates and the feasibility of other solutions. In this work, it is assumed that two images of the same...

Recognition of animals via images of their footprints is a non-invasive technique recently adopted by researchers interested in monitoring endangered species. One of the challenges that they face is the extraction of features from these images, which are required for this approach. These features are points along the boundary curve of the footprint...

Swarms consisting of cyborg-insects or millirobots can be used for mapping and exploration of unstructured environments in emergency-response situations. Under extreme conditions, traditional localization techniques may fail to provide reliable position estimates. Instead, we propose a robust approach to obtain a topological map of an unknown envir...

In this paper, we exploit minimal sensing information gathered from biologically inspired sensor networks to perform exploration and mapping in an unknown environment. A probabilistic motion model of mobile sensing nodes, inspired by motion characteristics of cockroaches, is utilized to extract weak encounter information in order to build a topolog...

The potential benefits of distributed robotics systems in applications requiring situational awareness, such as search-and-rescue in emergency situations, are indisputable. The efficiency of such systems requires robotic agents capable of coping with uncertain and dynamic environmental conditions. For example, after an earthquake, a tremendous effo...

Mobile sensor networks are often composed of agents with weak processing capabilities and some means of mobility. However, recent developments in embedded systems have enabled more powerful and portable processing units capable of analyzing complex data streams in real time. Systems with such capabilities are able to perform tasks such as 3D visual...