NSF National I-Corps UNC Charlotte Teams

UNc charlotte national science foundation national i-corps teams



Digital Biomarkers Combined with Wearable Devices to Monitor Alzheimer’s Disease and other Neurological Disorders

The broader impact/commercial potential of this I-Corps project is the development of a cloud-based, predictive analytics platform to identify, develop, and validate digital biomarkers that identify early signs of neurodegeneration and other significant health-related concerns related to aging. Existing methods of collecting longitudinal data from aging populations for clinical and nonclinical research are slow, expensive, labor-intensive and often introduce flawed data. This project may improve research with qualitative and quantitative behavioral and biophysical information using passive and unobtrusive patient-generated data aggregated in real-life environments using smartwatches. At scale, this platform may be used to build models that predict disease onset in at-risk populations and help millions of individuals around the world identify means to delay the onset or slow the progression of Alzheimer’s disease and related dementias. In addition, this platform may enable pharmaceutical companies with licensed access to data to conduct focused clinical trials in aging populations. The proposed technology may improve health-related applications offered by wearable/smartwatch manufacturers, and may provide health-concerned individuals with personalized low-cost behavior modification intervention recommendations in order to delay, prevent, or slow the progression of neurogenerative diseases and other health issues.

This I-Corps project is based on the development of analytical models deployable within the cloud to identify, measure, and predict those behavioral and clinical features impacting people at high-risk for Alzheimer’s Disease and related dementias. The proposed technology is an end-to-end patient monitoring solution that benefits patients, caregivers and medical researchers by combining data from widely available consumer smartwatches with a cloud-based analytics and artificial intelligence platform to deliver improved health and behavior information. This proposed software solution advances high-frequency data aggregation to a centralized cloud-based platform where it is possible to generate insights and identify, develop, and clinically validate digital biomarkers for Alzheimer’s disease, dementias, and other potential illnesses and comorbidities in aging populations. The development of this prototype artificial intelligence-driven platform may provide a solution to passively and unobtrusively capture patient-generated data beyond conventional clinical trials, in real-life settings, and advance capabilities to analyze digital health information from broad demographic populations.

Team Members:

Jon Corkey, Founder Amissa

Dr. Colby Ford

College of Computing and Informatics

A contactless, non-intrusive, artificial intelligence (AI)-enabled contact tracing system for reducing the spread of viruses

The broader impact/commercial potential of this I-Corps project is to use recent advances in artificial intelligence (AI) and deep learning to enhance public health challenges in nursing homes. Fever, as a non-specific measure of infection, is commonly observed in a broad range of diseases and pandemics. The proposed AI-powered assessment system will create an effective, non-intrusive tool for empowering nursing home facilities and clinics to combat the spread of contagious diseases and future pandemics, all the while providing a higher health resiliency for our communities. The proposed technology creates a real-time health surveillance system that also may be adopted and customized to a wide range of public health applications that require continuous, non-intrusive health monitoring with predictive analytics and proactive decision making. The proposed research has significant opportunities both in the public and private sectors.

This I-Corps project is based on the development of a monitoring system to mitigate the risk and control the spread of epidemic viruses through real-time artificial intelligence, multi-sensor fusion, and video data analytics. In contrast to existing approaches that have a narrow focus with limited intelligence capabilities, the proposed technology offers a holistic solution to enable scalable, reliable symptom assessment and contact tracing from a distance with strict personal privacy measures ensured. By utilizing both red green blue (RGB) and thermal cameras (off-the-shelf products), it may be possible to provide a more precise system that is capable of monitoring several health indicators simultaneously; e.g., body temperature, respiratory rate, coughing, and sneezing while taking a non-intrusive approach. The proposed device is equipped with an AI-enabled contact tracing system for reducing the spread of viruses by identifying the potentially infected individuals at the early stage. For privacy-aware contact tracing, the plan is to leverage previously developed technology for real-time privacy built-in human pose estimation, re-identification, trajectory analysis, and activity recognition. The technology creates lightweight, end-to-end execution of real-time computer vision based on RGB cameras, with the ability to perform at a high frame rate on embedded and edge devices.

An animation demo of the product is provided here: https://youtu.be/c6FgyH6bEQ8

Team Members:

Dr. Mona Azarbayjani

Roshanak Ashrafi

College of Arts + Architecture

Knowledge Graph Embeddings-based Explainable Artificial Intelligence for Enterprise Performance Management

The broader impact/commercial potential of this I-Corps project is the development of an enterprise performance management (EPM) platform for investors, customers, suppliers, employees, and the community. The technology aims to broaden the scope of knowledge from financial-centric performance to an interdisciplinary framework of economic, social, psychological, and physical well-being concerning all stakeholders. In addition, the technology may democratize artificial intelligence (AI) to ordinary organizational managers who may not possess sophisticated analytics skills. The current AI models lack interactive and intuitive storytelling. Matching the hierarchical clustering of data with a causal knowledge graph, the proposed technology will prepare user data in a way that mimics a general manager’s intuitive thinking. The technology addresses a commercial gap in the market - that of a lack of prescriptive capability, that is, telling end-users what they should do. Data may be collected from different sources in an organization, so they are fragmented and the causal links are lost. The external source of a causal knowledge graph fills the gap by presenting and interpreting the hidden causal links in EPM data. The project seeks to help executives to prescribe interventions to enhance the well-being of all stakeholders.

This I-Corps project is based on the development of a knowledge graph embeddings-based platform for statistical and machine learning models of enterprise performance management (EPM) data. The technology is designed to engage natural language processing models to convert a massive volume of scientific research in organizational science into a causal knowledge graph, which will be embedded into a visual analytics platform to structure and interpret enterprise management data. The goal is to help EPM users by explaining the hidden causal pathways visually and intuitively to enable improvements in organizational management. The proposed technology combines research outcomes across organizational and computer sciences and involves two innovations: a scientific knowledge graph on causes-and-effects related to organizational performance and a new knowledge graph embeddings-based visualization technique to enable explainable AI (XAI). Hierarchical clustering is used to explicate the descriptions of variables in data and organize these descriptions. Causal hypotheses are automatically developed based on the known causal links in the knowledge graph and then empirically tested in statistical and machine learning models.

Team Members:

Dr. Victor Z. Chen

Wendy Long

Nasheen Nur

Belk College of Business

Low-Cost Holographic TelePresence System

The broader impact/commercial potential of this I-Corps project is to commercialize a low-cost holographic telepresence system for the professional sports industry. The system can effectively improve performance and reduce the cost for the sports clubs and coaches in recruiting and training athletes from anywhere in the world. The holographic telepresence may significantly improve the efficiency of remote working and online training and spur new use cases and services improving quality of life. Through this project, the team aims to translate their fundamental research on ubiquitous machine vision systems to the market place via customer discovery. The team plans to integrate the knowledge learned from this project into their courses, senior design projects, and the mentoring of graduate students. In this way, they will cultivate next-generation engineers and researchers who can translate technologies into commercial products and help grow the economy.

This I-Corps project explores the commercial potential of a low-cost holographic telepresence system. The holographic telepresence system involves core technologies from advanced wireless communications, edge computing systems, and computer vision. The current market segment for this project is the sports industry, specifically collegiate sports and professional gymnasiums. The outcomes of the project will provide insights into the commercialization of new technologies that enable future holographic communication systems including multi-camera intelligent fusion methods that reliably construct the point cloud (a set of data points in space) from multiple cameras with temporal consistency, real-time adaptive point cloud compression and transmission methods that efficiently compress the point cloud based on the requirement of the service quality, and real-time network-aware artificial intelligence (AI)-powered point cloud reconstruction and transmission that can be efficiently transmitted to the smart mixed reality (MR) glass via wireless links.

Team Members:

Dr. Tao Han

Pedro Regaldo

Chen Chen

William States Lee College of Engineering

Digital Platform for Informal Learning Experiences to Encourage Curiosity in STEM Career Paths

The broader impact/commercial potential of this I-Corps project is the development of a digital platform that transforms the way young children perceive and engage in STEM career exploration. Factors promoting STEM education, particularly for under-represented groups, have not been incorporated into personalized learning plans. This technology will use artificial intelligence to develop customized educational plans. This I-Corps project is based on the development and evaluation of a new approach to generate recommendations by combining a model of surprise with a model of user preference to deliver curiosity-inspiring recommendations. The proposed technology is a digital platform that empowers students to pursue STEM learning and careers. This project will incorporate artificial intelligence and informal learning practices to recommend learning modules on a personalized basis.

Team Members:

Abi Olukeye, Founder Smart Girls HQ

Dr. Mary Lou Maher

College of Computing and Informatics

Air Purification System for Reducing Indoor Volatile Organic Compounds

The broader impact/commercial potential of this I-Corps project is that the project can potentially reduce hospital bills on indoor air quality related illness, reduce carbon emissions, and improve the indoor work environment. People spend most of their time indoors, making it critical that we address indoor air quality. In a broader view, this air depolluting system improves the indoor environment in three aspects: reducing volatile organic compounds (VOCs), improving the illumination of buildings by natural light (daylighting), and lowering carbon emissions. Reducing indoor VOCs benefits occupants? health and well-being, especially for vulnerable groups such as asthma patients. A study conducted at Lawrence Berkeley Laboratory estimated that improved indoor air quality can save $6-14 billion from reduced respiratory disease, $1-4 billion from reduced allergies and asthma, $10-30 billion from reduced sick building syndrome related illness, and $20-160 billion from direct, non-health related worker performance loss. This system can also lead to noticeable productivity gains by improving daylighting quality. A study found that students under better daylighting showed improvement in test scores, for example, they were 20% faster in math and 26% faster in reading. Improving daylighting also reduces energy consumption in artificial lighting and thus lowers carbon emissions. Successful application of this system in commercial buildings is expected to contribute to national energy savings in the building sector up to $30 billion and 280 tons of carbon dioxide reduction. In short, this system is expected to improve the health of building occupants, increase productivity, and reduce carbon emissions.

This I-Corps project develops a novel air depolluting system coated with a thin layer of titanium dioxide nanoparticles, one of the most effective photo-induced catalysts to remove air pollutants. The system is designed to be installed in interior spaces behind windows in contact with indoor air while receiving UV-A rays coming through windows. The efficiency of titania breaking down VOCs primarily depends on three parameters: the dose of effective UV rays, contact surface area, and airflow. The efficiency monotonically increases as any one of the three parameters increases. The primary challenge is that improving one parameter can negatively affects the other two. To maximize overall VOC reduction, a multi-objective optimization algorithm is used to balances UV ray incident angles, contact surface area, and airflow. This air depolluting system is a completely passive system which means that it does not contain any mechanical/electrical device and does not require power to operate, which lowers initial costs and minimizes maintenance.

Team Members:

Dr. Chengde Wu

College of Arts + Architecture

Regeneration High-Performance Curtain Wall for Net Zero Energy Buildings

The broader impact/commercial potential of this I-Corps project is the development of a cost-effective regenerative curtain wall specifically configured to achieve building energy cost-savings and user satisfaction for net-zero energy building applications. Building envelopes contribute to an increase in heating, cooling, and lighting loads inside the building while affecting occupant comfort. This technology, a high-performance regenerative curtain wall, is designed to curtail building energy consumption and carbon dioxide (CO2) emissions to reduce broader societal, economic, and commercial impacts. The successful application of the technology in commercial buildings is expected to accomplish 95 trillion Btu in annual energy reduction (i.e., $10 billion energy bill saving) and 13 million metric tons of CO2 sequestration. This I-Corps project is based on the development of a multi-functional, energy-efficient curtain wall system.

This regenerative curtain wall incorporates a 3D interlayer of concentrated micro-photovoltaic (CMPV) components within a double-pane glass assembly. Capitalizing on solar radiation, CMPV components consist of silicone-based solar cells with a Fresnel lens, which is a lightweight optical lens whose light concentration efficiency maximizes solar output within a small PV cell area. A key advantage of using the CMPV is that it allows optimization for various building performance needs, including energy efficiency (i.e., reduction of air conditioning load and maximum daylight transmission), user satisfaction (i.e., temperature, relative humidity, glare), and solar-powered energy production. The optimization algorithm for geometric configurations of the system may maximize power production and year-round energy savings for different climate conditions and building orientations where sunlight level varies. Reductions in building operational energy costs from lighting, cooling, and heating loads may result in energy cost savings. In addition, the system?s ability to allow viewing and daylight penetration is expected to improve the health and well-being of building occupants.

Team Members:

Dr. Kyoung-Hee Kim

College of Arts + Architecture

Hexacoordinate pincer complexes for organic electronic devices

The broader impact/commercial potential of this I-Corps project is to develop materials for organic electronic devices. Relevant applications include organic light emitting diodes (OLEDs), organic photovoltaic cells (OPVs), large panel displays, and implantable/wearable biotech devices. The switch to organic electronics offers flexible, lighter, cheaper, and more sustainable devices. The materials being developed offer customized charge transport properties optimized for the various needs of the electronics industry sectors. The materials are compatible with current material processing techniques and are ready for rapid integration into existing device manufacturing streams. These materials could enhance the device efficiency, leading to devices that can last longer before needing to be recharged, as well as device lifetime. This I-Corps project is based on the hexacoordinate Si(pincer)2 platform, uniquely suited for synthetic tailoring to meet the demand for low-cost materials with improved charge mobility in the organic electronics industry. The push-pull, charge transport nature of the pincer ligands allow for selective tuning of the material's HOMO or LUMO levels, accurately predicted through molecular modeling. The molecular geometry of the Si(pincer)2 complexes enhances solid state packing efficiency to facilitate charge transport, while also ensuring negligible dipole moments and higher vapor pressure for thermal evaporation. The hexacoordinate silicon center also enforces planarity of the dianionic pincer ligands. The materials are compatible with current thermal evaporation techniques

Team Members:

Dr. Thomas Schmedke

Dr. Margaret Kocherga

College of Liberal Arts & Sciences

Polymer Semiconductor Educational Kits

The broader impacts of this I-Corps project are to improve hands-on laboratory experiences in the field of materials science and to increase STEM awareness for students/instructors at the 9-12 and undergraduate levels. The educational kit bridges the gap between theoretical and practical molecular materials technologies. The kit is an interactive learning tool designed for interdisciplinary laboratory activities and can be used in physics, chemistry, engineering, and material science educational settings. The kits provide all needed materials, a fully developed curriculum, and training for implementation. Professional development workshops for instructors help to integrate the laboratory activities into instructor?s existing science curriculum while addressing national and international science standards. There is a significant market for hands-on laboratory activities that incorporate contemporary science experiments currently under investigation and development at the university level. School districts and science / engineering departments will likely be interested in acquiring the kits, making them a commercially impactful and important education platform. The kit is flexible and can be expanded upon to include future experiments and professional development. The kits can also be used and developed for extracurricular activities such as science fairs and competitions.

This I-Corps project involves a polymer electronics laboratory kit to improve materials science education for 9-12 and undergraduate students. The three-module kit and curriculum use polymer semiconductors to provide hands-on inquiry activities integrating themes of electrical conductivity, light emission, and light-harvesting solar energy conversion. These themes are critical to contemporary materials science research and education. The kit includes materials to evaluate the electrical properties of conductive colloidal polyaniline inks, to construct a polymer light- emitting diode using polyphenylene vinylene, and to build a polymer solar cell using semiconductive polymers and nanoparticulate TiO2. Designed initially for high school science classrooms, the activities developed also meet new collegiate undergraduate education requirements for macromolecular, supramolecular, and nanoscale systems in the curriculum and can be used in undergraduate teaching laboratories. The modules and kit have also been implemented in professional development workshops for training 9−12 science educators to help integrate the laboratory activities into their curriculum.

Team Members:

Dr. Michael Walter

Dr. Meesha Kaushal

College of Liberal Arts  & Sciences

FlySmart Robotics

In this project we design efficient algorithms fo motion planning for multiple of UAVs. The applications are coverage of linear features in the environmnet, such as road network or a power line distribution. The technology will provide fast and easy to use systems to design flight plans for multiple UAVs. 

Team Members:

Dr. Srinivas Akella

Saurav Agarwal

Thao Tran

College of Computing and Informatics

Upcoming National Participants