Dr. Alper Bozkurt performs research on how microsystems based tissue interfaces and physiological monitoring systems can be used to wirelessly interact with animals and crops that is raised in agricultural fields to enable new phenotypical data for smarter farming. This is one of the on-going efforts under Integrated Bionic MicroSystems Laboratory (iBionicS Lab) which has a vision to introduce conceptually novel animal-plant-machine interfaces to bridge artificial systems with biological organisms towards the next generation bionic cyber-physical systems. Such cyber-physical systems would be the building blocks of a new era where everything is connected to each other through the Internet of Things.
Biography: Alper Bozkurt is a professor of Electrical and Computer Engineering and University Faculty Scholar at NC State University. He received a Ph.D. degree from Cornell University in 2010 working with Prof. Amit Lal on DARPA’s Hybrid Insect Micro-Electro-Mechanical-Systems program. His research team at NC State performs research on connecting biological organisms to the cloud to solve real life engineering problems in innovative ways. The ongoing funded projects include “insect-machine-interfaces” with remotely controlled biobotic insects for exploration and mapping after natural disasters, “canine-machine interfaces” to enable a computer assisted canine training system and remotely interact with canines and “plant-machine interfaces” to record biopotentials and impedances on crops and trees to monitor their stress response. His recent achievements were covered by media including BBC, CNN, National Geographic, Discovery Channel and Reuters. Bozkurt is a recipient of Calhoun Fellowship from Drexel University, Donald Kerr Award from Cornell University, Chancellor’s Innovation Award and William F. Lane Outstanding Teacher Award at NC State, the CAREER Award from National Science Foundation, IBM Faculty Award, IEEE Sensors Council Young Professional Award and was included in Popular Science Magazine’s Brilliant 10 list. His research team received best paper awards from The US Government Microcircuit Applications & Critical Technology Conference, IEEE Body Sensor Network Conference and IEEE Sensors Conference.
SHORT DESCRIPTION OF INTERESTS:
Dr. Bozkurt joined NCSU in August 2010. His research interests include development of microscale sensors, actuators and methodologies to unlock the mysteries of biological systems with an aim of engineering these systems directly or developing new engineering approaches by learning from these systems. His work related to Coastal Resilience Initiative includes behavioral monitors for mussels, physiological sensors for fishes, stress detection in plants and smart trapping of insects. His team develops novel sensors and provides wireless electronics prototyping support.
- A Wireless Multimodal Physiological Monitoring ASIC for Injectable Implants , IEEE 49TH EUROPEAN SOLID STATE CIRCUITS CONFERENCE, ESSCIRC 2023 (2023)
- Continuous heart rate variability monitoring of freely moving chicken through a wearable electrocardiography recording system , POULTRY SCIENCE (2023)
- Robust Cough Detection With Out-of-Distribution Detection , IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2023)
- A Multimodal Sensing Platform for Interdisciplinary Research in Agrarian Environments , SENSORS (2022)
- Preliminary Evaluation of a System with On-Body and Aerial Sensors for Monitoring Working Dogs , SENSORS (2022)
- Real-Time Monitoring of Plant Stalk Growth Using a Flexible Printed Circuit Board Sensor , 2022 IEEE SENSORS (2022)
- Wireless Wearable Electrochemical Sensing Platform with Zero- Power Osmotic Sweat Extraction for Continuous Lactate Monitoring , ACS SENSORS (2022)
- A Wearable Patch for Prolonged Sweat Lactate Harvesting and Sensing , 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) (2021)
- Evaluation of Environmental Enclosures for Effective Ambient Ozone Sensing in Wrist-worn Health and Exposure Trackers , 2021 IEEE SENSORS (2021)
- Investigating the Relationship between Cough Detection and Sampling Frequency for Wearable Devices , 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) (2021)
The ultimate vision of our research is to establish a new architecture and design toolbox for Reconfigurable Metal-Free Microsystems with Alternative Power Sources (RMF-MAPS), amenable to modular assembly, green material technologies, hybrid production processes, and next-generation energy technologies. The outcomes will inform the engineering community of new research directions and technologies to disrupt conventional microelectronic technology into the next century. Specifically, we propose to demonstrate a 100% metal-free sensing-communications node, which operates from a renewable energy source and is completely degradable or recyclable after a designed operational lifetime.
In this proposal, we aim to study and develop a transformative plant wearable sensor that can be deployed on-plant for continuous monitoring of biotic and abiotic stresses of plants and their microenvironment to inform plant health status and early detection of plant diseases. This multifunctional plant wearable sensor will include an array of ligand-functionalzied chemiresistive sensors to profile plant leaf VOCs and nanowire-based flexible sensors to monitor microclimate in parallel. The sensors will be prepared on a light-transparent, gas-permeable, and stretach substrate for long-term wearibility on live plants. In addition, a signal transmitter will be developed for wireless data acquistion and transmission. The system will be thourughly tested on tomato plants in the greenhouse for stress monitoring and disease detection.
Accurate monitoring for changes in pest susceptibility to insecticidal toxins expressed in genetically engineered agronomic crops is currently an ineffective process limited by both scale and scope of deployment. Although long-term scientific and social change will be necessary to minimize pest resistance evolution, understanding near-term shifts in susceptibility through novel monitoring will also be essential to enable more effective resistance management strategies. To address this limitation on resistance monitoring, we propose to develop and deploy real-time pheromone-based sensor platforms to indicate patterns of lepidopteran pest activity in landscapes. We will use cotton bollworm (Helicoverpa zea Boddie) as a case study to develop and refine automated monitoring tools designed to detect shifts in pest susceptibility.
Advanced Self-powered Systems of Integrated Sensor Technologies (ASSIST) vision is to be a dynamic leader in development of wearable, self-powered integrated sensor technologies for continuous health and environmental monitoring. These technologies will directly respond to NAE?s Grand Challenge to advance health informatics to improve acquisition, management and use of health information to enhance medical care, correlate disease and environment and revolutionize response to public health emergencies, disasters, pandemics and/or chem-bio attacks. ASSIST?s mission is to transform US and global health informatics, electronics and biomedical engineering industries through development and demonstration of fundamental and enabling nanotechnologies for energy harvesting, battery-free energy storage and ultra-low power computation and communication, integrated with physiological and environmental nanosensors and biocompatible materials, to empower personal environmental health monitoring and emergency response. Goals: 1. Advance discovery in energy harvesting and storage, multifunctional sensors and materials, and low-power systems design; 2. Develop enabling technologies for energy conversion, device reliability and ultra-low power computation and communications, with integration to achieve two 1st-generation test-beds: self-sustaining wireless nodes and conformal multifunctional applications; 3. Develop systems integration requirements and demonstrate ?Exposure Track? and ?Emergency Track? testbeds; 4. Develop efficient and secure methods to handle large quantities of data and retrieve patterns of environmental and health correlations; 5. Create a culture of team-based research, education and innovation, cultivating a diverse group of talented, well prepared graduates excited about research, design and production of health informatics and biomedical engineering solutions to improve global health and safety; 6. Form partnerships with precollege institutions to strengthen the STEM pipeline by helping middle and high school students and teachers develop technical literacy and motivation to contribute to solving NAE Grand Challenges; 7. Stimulate entrepreneurship and form sustainable partnerships with small and large firms, health practitioners and emergency responders to link ASSIST discoveries to innovation, accelerated commercialization and job creation. ASSIST integrated sensor technologies will result in a wearable health patch that incorporates energy harvesting and storage, computation and communication, along with low-power integrated sensors for health and environmental exposures. This will be the platform technology that will drive two systems applications related to global health. The first, the Exposure Track, will enable longitudinal, simultaneous monitoring of environmental factors and human health parameters to create an unprecedented set of data to lead to direct understanding of how environment impacts health. This information, of great interest to EPA and CDC, will revolutionize our understanding of environmental health and may impact future regulatory policies. The patch?s self-powered nature will enable critical longitudinal monitoring. This system will involve epidemiologists, social scientists, data mining, pattern recognition professionals and EPA scientists to further understanding of environmental health. The patch will also drive a second system, the Wellness Track, which will aim to empower patients to take charge of their own health by having readily accessible information about their health status. According to the Milken Inst., lifestyle diseases consume 70% of the US?s health care resources and face an unsustainable future in light of rising health care costs. It has been shown that humans are more likely to change lifestyle habits if they witness real-time, positive changes in their health as a result of those changes. The Wellness Track will provide an unobtrusive, battery-free interface for sensing of multiple vital signs, along with advanced and secure communication strateg
Our main goals are to: (1) develop a statistically-sound data-driven framework for signal quality characterization of wearable devices in the real-world; and (2) use this framework for enhancing algorithmic developments and hardware design. Current approaches depend on rules or indicators derived from expert knowledge in controlled environments, so they do not generalize well to the use at-home. Our main application will be the early asthma exacerbation detection. We aim to employ the prototypes designed by the NSF-ERC ASSIST center, which aims to develop nano-enabled energy harvesting, energy storage, nanodevices and sensors to create innovative battery-free, body-powered, and wearable health monitoring systems.
This research project aims to test a class of minimally invasive biosensing microsystems that extract, process, and analyze sweat and interstitial fluid (ISF) monitor metabolic profiles in a more continuous and longitudinal method.
NIRSense is an early stage R&D company designing somatic and cerebral oximetry devices using functional near-infrared spectroscopy (fNIRS). The proposed SBIR project directly supports research on this platform to ideate on a deployable prototype for use toward cognitive state monitoring using combined NIRS and electroencephalography (EEG). The specific objectives of this proposal are to understand the unique requirements for combined NIRS+EEG in a forehead worn system suitable for use in military tactical jets.
We propose a novel sensor system with accompanying data analytics to explore the capability of wearable multimodal sensors to address the short-comings of the traditional polysomnography systems. If successful, this project will lead to improved capacity to carry out sleep research and to detect and treat sleep disorders. The miniaturization and low power consumption will pave the way for rapid adoption and deployment of these systems for home-use in real-world settings.
Stroke is a leading cause of motor disability. A majority of stroke survivors exhibit upper and lower limb motor impairments, ranging from incapability of reaching and grasping objects to limited ambulation. The objective of this project is to develop a personalized, community-based rehabilitation system to improve daily functions of stroke survivors. The system will include three essential components ÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“ a nanomaterial-enabled multifunctional wearable sensor network to monitor arm and leg functional activities; a low-power data acquisition, processing, and transmission protocol; and a user interface (i.e., smart phone APP) to communicate training outcomes to the users and clinicians and receive feedback from the users and clinicians. The proposed community-based rehabilitation system will enable personalized, continuous rehabilitation during daily activities.
Recent developments in miniature, low-power wireless sensors has provided systems capable of long-term deployment and continuous operation. Plant sciences will be benefited by the application of such a tailored suite of sensors, in which the output data will inform real-time changes to growth conditions in order to minimize cost and maximize yield. This proposal develops a physical and computational framework that will determine the necessary suite of plant physiology sensors and uses the collected data to model the complex interactions between phenotypical response and growth conditions. The contributions of this award will facilitate the broad adoption of new plant physiology sensors and analytic platforms for plant/crop management.