Computer Engineering Commons^™

Feel And Touch: A Haptic Mobile Game To Assess Tactile Processing, Ivonne Monarca, Monica Tentori, Franceli L. Cibrian

Engineering Faculty Articles and Research

Haptic interfaces have great potential for assessing the tactile processing of children with Autism Spectrum Disorder (ASD), an area that has been under-explored due to the lack of tools to assess it. Until now, haptic interfaces for children have mostly been used as a teaching or therapeutic tool, so there are still open questions about how they could be used to assess tactile processing of children with ASD. This article presents the design process that led to the development of Feel and Touch, a mobile game augmented with vibrotactile stimuli to assess tactile processing. Our feasibility evaluation, with 5 children …

A Kinetic Model For Blood Biomarker Levels After Mild Traumatic Brain Injury, Sima Azizi, Daniel B. Hier, Blaine Allen, Tayo Obafemi-Ajayi, Gayla R. Olbricht, Matthew S. Thimgan, Donald C. Wunsch

Mathematics and Statistics Faculty Research & Creative Works

Traumatic brain injury (TBI) imposes a significant economic and social burden. The diagnosis and prognosis of mild TBI, also called concussion, is challenging. Concussions are common among contact sport athletes. After a blow to the head, it is often difficult to determine who has had a concussion, who should be withheld from play, if a concussed athlete is ready to return to the field, and which concussed athlete will develop a post-concussion syndrome. Biomarkers can be detected in the cerebrospinal fluid and blood after traumatic brain injury and their levels may have prognostic value. Despite significant investigation, questions remain as …

Caries And Restoration Detection Using Bitewing Film Based On Transfer Learning With Cnns, Yi-Cheng Mao, Tsung-Yi Chen, He-Sheng Jhou, Szu-Yin Lin, Sheng-Yu Liu, Yu-An Chen, Yu-Lin Liu, Chiung-An Chen, Yen-Cheng Huang, Shih-Lun Chen, Chun-Wei Li, Patricia Angela R. Abu, Wei-Yuan Chiang

Department of Information Systems & Computer Science Faculty Publications

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early; the treatment will be relatively easy; which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However; the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of …

Personalized Digital Phenotype Score, Healthcare Management And Intervention Strategies Using Knowledge Enabled Digital Health Framework For Pediatric Asthma, Utkarshani Jaimini, Amit Sheth

Publications

Asthma is a personalized, and multi-trigger respiratory condition which requires continuous monitoring and management of symptoms and medication adherence. We developed kHealth: Knowledge-enabled Digital Healthcare Framework to monitor and manage the asthma symptoms, medication adherence, lung function, daily activity, sleep quality, indoor, and outdoor environmental triggers of pediatric asthma patients. The kHealth framework collects up to 1852 data points per patient per day. It is practically impossible for the clinicians, parents, and the patient to analyze this vast amount of multimodal data collected from the kHealth framework. In this chapter, we describe the personalized scores, clinically relevant asthma categorization using …

Characterization Of Time-Variant And Time-Invariant Assessment Of Suicidality On Reddit Using C-Ssrs, Manas Gaur, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonathan Beich, Jyotishman Pathak, Amit Sheth

Publications

Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential - most dramatically envisioned as a trigger …

Bibliometric Survey On Effect Of Socio-Economic Factors On Spread Of Corona Virus (Covid-19), Seema Patil Prof., Aayushi Verma Ms, Isha Patil Ms, Ravneesh Singh Mr, Raghav Gaur Mr

Library Philosophy and Practice (e-journal)

The Novel Coronavirus disease has been rapidly spreading all around the globe, from the time when it was first reported in the Wuhan city of China. The primary focus of this bibliometric survey is to distinguish the documents which have hypothesized and expanded on the effects of various socio-economic factors when it comes to the spread of the Coronavirus.

This survey does the evaluation on the 480 documents found. The United Kingdom of the Great Britain and United States have contributed the largest number of publications in this field of research followed closely by India and Italy.

The survey includes …

Bibliometric Analysis Of Emerging Technologies In The Field Of Computer Science Helping In Ovarian Cancer Research, Sonali Kothari Dr., Anvita Gupta, Muskaan Agrawal Agrawal, Kajal Jaggi, Adhiraj Dev Goswami, Ketan Kotecha, M. Karthikeyan Dr., Vijayshri Khedkar

Library Philosophy and Practice (e-journal)

This study is carried out to provide an analysis of the literature available at the intersection of ovarian cancer and computing. A comprehensive search was conducted using Scopus database for English-language peer-reviewed articles. The study administers chronological, domain clustering and text analysis of the articles under consideration to provide high-level concept map composed of specific words and the connections between them.

Quantitative Analysis Of Research On Artificial Intelligence In Retinopathy Of Prematurity, Ranjana Agrawal, Manasi Anup Agrawal, Sucheta Kulkarni, Ketan Kotecha, Rahee Walambe

Library Philosophy and Practice (e-journal)

Retinopathy of Prematurity (ROP) is a disease of the eye and a potential source of blindness in low birth weight preterm infants. It is preventable if diagnosed and treated on time. Artificial Intelligence (AI) has played an important role in developing automated screening systems to assist medical experts. There are many traditional literature review articles available that focus on the scientific content of ROP-AI. The researchers also require a bibliometric analysis to become acquainted with the competing groups and new trends in this field. This paper gives a brief overview of ROP and AI systems for ROP screening with a …

Assistive Robotics And Their Uses During The Pandemic, Thomas Klassen, Jeremy Evert

Student Research

• “Assistive Robotics” defines any device that can sense, process sensory information, and perform actions that benefit people with disabilities.

• This form of technology can be used on a much higher scale with a greater number of uses.

• We have an opportunity to expand the usage of assistive robotics to help combat COVID-19.

Detection Of Myocardial Infarction Using Ecg And Multi-Scale Feature Concatenate, Jia-Zheng Jian, Tzong-Rong Ger, Han-Hua Lai, Chi-Ming Ku, Chiung-An Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However; issues; particularly overfitting and underfitting; were not being taken into account. In other words; it is unclear whether the network structure is too simple or complex. Toward this end; the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally; multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being …

Semantics Of The Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable And Explainable?, Manas Gaur, Keyur Faldu, Amit Sheth

Publications

The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. The DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of DL models and their over-reliance on massive amounts of data condensed into labels and dense representations poses challenges for interpretability and explainability of the system. Furthermore, DLs have not yet been proven in their ability to …

Video Analysis And Verification Of Direct Head Impacts Recorded By Wearable Sensors In Junior Rugby League Players, Lauchlan Carey, Douglas P. Terry, Andrew S. Mcintosh, Peter Stanwell, Grant L. Iverson, Andrew J. Gardner

Research outputs 2014 to 2021

Background: Rugby league is a high-intensity collision sport that carries a risk of concussion. Youth athletes are considered to be more vulnerable and take longer to recover from concussion than adult athletes. Purpose: To review head impact events in elite-level junior representative rugby league and to verify and describe characteristics of X-patchTM-recorded impacts via video analysis. Study Design: Observational case series. Methods: The X-patchTM was used on twenty-one adolescent players (thirteen forwards and eight backs) during a 2017 junior representative rugby league competition. Game-day footage, recorded by a trained videographer from a single camera, was synchronised with X-patchTM-recorded timestamped events. …

Towards Better Remote Healthcare Experiences: An Mhealth Video Conferencing System For Improving Healthcare Outcomes, El Sayed Mahmoud, Edward Sykes, Blake Eram, Sandy Schwenger, Jimmy Poulin, Mark Cheers

Publications and Scholarship

This work investigated how to combine mobile cloud computing, video conferencing and user interface design principles to promote the effectiveness and the ease of using online healthcare appointment platforms. The Jitsi Meet video conference technology was selected from amongst 27 competing systems based on efficiency and security criteria. This platform was used as the foundation on which we designed, developed and evaluated of our video conferencing system specially designed for improving doctor-patient interaction and experiences. Nine doctor- patient functions were developed in order to facilitate efficient and effective online healthcare appointments, such as providing the doctor with the ability to …

3d Reconstruction Of Spine Image From 2d Mri Slices Along One Axis, Somoballi Ghoshal, Sourav Banu, Amlan Chakrabarti, Susmita Sur-Kolay, Alok Pandit

Journal Articles

Magnetic resonance imaging (MRI) is a very effective method for identifying any abnormality in the structure and physiology of the spine. However, MRI is time consuming as well as costly. In this work, the authors propose an algorithm which can reduce the time of MRI and thus the cost, with minimal compromise on accuracy. They reconstruct a three-dimensional (3D) image of the spine from a sequence of 2D MRI slices along any one axis with reasonable slice gap. In order to preserve the image at the edges properly, they regenerate the 3D image by using a combination of bicubic and …

Supporting Coordination Of Children With Asd Using Neurological Music Therapy: A Pilot Randomized Control Trial Comparing An Elastic Touch-Display With Tambourines, Franceli L. Cibrian, Melisa Madrigal, Marina Avelais, Monica Tentori

Engineering Faculty Articles and Research

Aim

To evaluate the efficacy of Neurologic Music Therapy (NMT) using a traditional and a technological intervention (elastic touch-display) in improving the coordination of children with Autism Spectrum Disorder (ASD), as a primary outcome, and the timing and strength control of their movements as secondary outcomes.

Methods

Twenty-two children with ASD completed 8 NMT sessions, as a part of a 2-month intervention. Participants were randomly assigned to either use an elastic touch-display (experimental group) or tambourines (control group). We conducted pre- and post- assessment evaluations, including the Developmental Coordination Disorder Questionnaire (DCDQ) and motor assessments related to the control of …

Circus In Motion: A Multimodal Exergame Supporting Vestibular Therapy For Children With Autism, Oscar Peña, Franceli L. Cibrian, Monica Tentori

Engineering Faculty Articles and Research

Exergames are serious games that involve physical exertion and are thought of as a form of exercise by using novel input models. Exergames are promising in improving the vestibular differences of children with autism but often lack of adaptation mechanisms that adjust the difficulty level of the exergame. In this paper, we present the design and development of Circus in Motion, a multimodal exergame supporting children with autism with the practice of non-locomotor movements. We describe how the data from a 3D depth camera enables the tracking of non-locomotor movements allowing children to naturally interact with the exergame . A …

Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han

Public Health Faculty Publications

The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …

Is It Safe For My Child’S Asthma?, Utkarshani Jaimini, Amit Sheth, Krishnaprasad Thirunarayan, Maninder Kalra, Marco Valtorta

Publications

kHealth-Asthma, a personalised digital healthcare framework is developed to address the above shortcomings by continuous monitoring of the child’s digital phenotype, indoor, and outdoor environmental data. The kHealth-Asthma study has recruited 140 children (ongoing) with an aim to complete recruitment of 150 children. The study period is either 1 month or 3 month depending on the choice of the study participant. kHealth-Asthma collects 29 multi-modal parameters leading to 1852 data points per patient per day (i.e. deployment: 1 month:1852*30=55,560 data points per patient and 3 month:1852*90=166,680 data points per patient). The digital phenotype collected using the kHealth-Asthma generates a Digital …

Mining User-Generated Content Of Mobile Patient Portal: Dimensions Of User Experience, Mohammad Al-Ramahi, Cherie Noteboom

Faculty Research & Publications

Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. The incorporation of patient portals provides the promise to deliver excellent quality, at optimized costs, while improving the health of the population. This study extends the existing literature by extracting dimensions related to the Mobile Patient Portal Use. We use a topic modeling approach to systematically analyze users’ feedback from the actual use of a common mobile patient portal, Epic’s MyChart. Comparing results of Latent Dirichlet Allocation analysis with those of human analysis validated the extracted …

Non-Obstetrical Robotic-Assisted Laparoscopic Surgery In Pregnancy: A Systematic Literature Review., Courtney Capella, Joseph Godovchik, Thenappan Chandrasekar, Huda B. Al-Kouatly

Department of Urology Faculty Papers

Urologic and gynecologic surgeons are the top utilizers of robotic surgery; however, non-obstetrical robotic-assisted laparoscopic surgery (RALS) in pregnant patients is infrequent. A systematic literature review was performed to ascertain the frequency, indication and complications of RALS in pregnancy. Results showed thirty-eight pregnancies from eleven publications between 2008-2020. Five cases were for urologic indication and thirty-three for gynecologic indication. Minimal surgical alterations were required. Although no adverse maternal-fetal outcomes were reported, there are not enough cases published to determine safety. This review demonstrates the feasibility of RALS for the pregnant population in the hands of competent robotic surgeons.

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

Supporting Self-Regulation Of Children With Adhd Using Wearables: Tensions And Design Challenges, Franceli L. Cibrian, Kimberley D. Lakes, Arya Tavakoulnia, Kayla Guzman, Sabrina Schuck, Gillian R. Hayes

Engineering Faculty Articles and Research

The design of wearable applications supporting children with Attention Deficit Hyperactivity Disorders (ADHD) requires a deep understanding not only of what is possible from a clinical standpoint but also how the children might understand and orient towards wearable technologies, such as a smartwatch. Through a series of participatory design workshops with children with ADHD and their caregivers, we identified tensions and challenges in designing wearable applications supporting the self-regulation of children with ADHD. In this paper, we describe the specific challenges of smartwatches for this population, the balance between self-regulation and co-regulation, and tensions when receiving notifications on a smartwatch …

El Artista Está En Línea: E-Performance En El Tiempo De Covid-19, Ezequiel N. González

Independent Study Project (ISP) Collection

Esta investigación propone una lectura detallada y comparativa de varias e-performances creadas por el artista visual uruguayo Ernesto Rizzo y la performer argentina Susy Shock, trazando un corpus de trabajo creado durante la crisis de COVID-19 y compartido en Instagram desde finales de marzo a finales de mayo de 2020. Al centrarse en el giro digital del arte de performance debido a las particularidades de la cuarentena, esta investigación busca distinguir este momento de "e-performance", entendiendo cuáles serán sus ramificaciones para el/la artista y el arte de performance en general. Así, a través de una reflexión teórica sobre el terreno …

Learning In The Machine: To Share Or Not To Share?, Jordan Ott, Erik Linstead, Nicholas Lahaye, Pierre Baldi

Engineering Faculty Articles and Research

Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sharing is necessary. If so, to which degree of precision? If not, what are the alternatives? The goal of this study is to investigate these questions, primarily through simulations where the weight-sharing assumption is relaxed. Taking inspiration from neural circuitry, we explore the use of Free Convolutional Networks and neurons with variable connection patterns. Using Free Convolutional Networks, we show that while weight-sharing is a pragmatic optimization …

Scalable Profiling And Visualization For Characterizing Microbiomes, Camilo Valdes

FIU Electronic Theses and Dissertations

Metagenomics is the study of the combined genetic material found in microbiome samples, and it serves as an instrument for studying microbial communities, their biodiversities, and the relationships to their host environments. Creating, interpreting, and understanding microbial community profiles produced from microbiome samples is a challenging task as it requires large computational resources along with innovative techniques to process and analyze datasets that can contain terabytes of information.

The community profiles are critical because they provide information about what microorganisms are present in the sample, and in what proportions. This is particularly important as many human diseases and environmental disasters …

A Hybrid Agent-Based And Equation Based Epidemiological Model For The Spread Of Infectious Diseases, Elizabeth Hunter

Doctoral

Infectious disease models are essential in understanding how an outbreak might occur and how best to mitigate an outbreak. One of the most important factors in modelling a disease is choosing an appropriate model and determining the assump tions needed to create the model. The main research questions this thesis addresses are how do we create a model for the spread of infectious diseases that captures heterogeneous agents without using an inordinate amount of computing power and how can we use that model to plan for future infectious disease outbreaks. We start our work by analysing and comparing equation based …

Applications Of Cloud-Based Quantum Computers With Cognitive Computing Algorithms In Automated, Evidence-Based Virginia Geriatric Healthcare, Henry Childs

Auctus: The Journal of Undergraduate Research and Creative Scholarship

Quantum computers have recently headlined IBM’s next generation of products promoting computational evolution. After the successful release of the cloud-streaming quantum computer IBM Watson Q, the company has released projections for future development of quantum devices. Because of the incredible processing power of these machines and the expected integration into everyday life in the near future, what implications can this have in the healthcare field?

I am studying cloud-based quantum computers with natural language processing (NLP) algorithms and patient health record data because I want to understand automated, evidenced-based co-optimized treatment of home-bound geriatric patients in order to help my …

Automated Assessment Of Cardiothoracic Ratios On Chest Radiographs Using Deep Learning, Varun Danda, Paras Lakhani, Md

Phase 1

Introduction: The cardiothoracic ratio (CTR) is a quantitative measure of cardiac size that can measured from chest radiography (CXR). Although radiologists using digital workstations possess the ability to calculate CTR, clinical demands prevent calculation for every case. In this study, the efficacy of a deep convolutional neural network (dCNN) to assess CTR was evaluated.

Methods: 611 HIPAA-compliant de-identified CXRs were obtained from [institution blinded] and public databases. Using ImageJ, a board-certified radiologist (reader #1) and a medical student (reader #2), measured the CTR by marking four pixels on all CXRs: the right- and left-most chest wall, the right- and left-most …

Internet Of Things For Sustainable Human Health, Abdul Salam

Faculty Publications

The sustainable health IoT has the strong potential to bring tremendous improvements in human health and well-being through sensing, and monitoring of health impacts across the whole spectrum of climate change. The sustainable health IoT enables development of a systems approach in the area of human health and ecosystem. It allows integration of broader health sub-areas in a bigger archetype for improving sustainability in health in the realm of social, economic, and environmental sectors. This integration provides a powerful health IoT framework for sustainable health and community goals in the wake of changing climate. In this chapter, a detailed description …

Assessment Of Dobhoff Tube Malposition On Radiographs Using Deep Learning, Kevin George, Paras Lakhani, Md

Phase 1

Introduction: Dobhoff tubes (DHT) are narrow-bore flexible devices that deliver enteral nutrition for critically ill patients. Tracheobronchial insertion of DHTs presents a significant risk for pulmonary complications. Thus, DHT insertion requires radiologist confirmation of correct placement with chest x-ray (CXR), increasing clinical delays. To address this, we demonstrate the novel application of Deep Convolutional Neural Networks (DCNNs) to automatically and accurately identify DHTs in CXRs in real time.

Methods: 141 de-identified HIPAA compliant frontal view chest radiographs containing DHTs in various positions were obtained. The DHTs were first manually segmented and verified by a board certified radiologist. Images were split …