Toward personalized assistive robotics: How to give robots human-like intuitions about movements
Dr. Reza Sharif Razavian
November 29, 2023 | 9:00 am CST
Humans, in addition to their remarkable dexterity and motor coordination, have the ability to intuitively understand each other’s movements. This intuition is the reason why two friends can carry a table in complete synchrony without the table slipping out of their hands. How can we give robots similar intuition about human movements? This can lead to more intelligent assistive and rehabilitation robots—those that can autonomously infer their user’s intentions and capabilities and provide optimal and personalized care. The focus of my research team is to develop such human-aware robotic systems. In my seminar, I provide a quick tour of the broad pieces of knowledge that need to come together to enable human-aware robotics. I will present my past results under three major research thrusts. First, I discuss the details of an intelligent robotic system with a built-in mechanism to estimate the internal neuromuscular states of the user, as the first step toward personalized assistive robotics. I will further present my results in developing mechanistic models for the human neuromuscular control system. Finally, I move up the human motor control hierarchy and present the principles in movement strategy selection when humans face complex tasks. I will conclude my presentations by providing a roadmap to bring these insights into the unified framework for designing personalized robotic systems.
Dr. Reza Sharif Razavian is an Assistant Professor of Mechanical Engineering at Northern Arizona University. His expertise is in robotics and control, with a deep interest in neural control of human movements. Dr. Razavian’s pioneering research conjoins state-of-the-art robotics and control algorithms with the latest neuroscientific theories and musculoskeletal models to advance the field of human-robot interaction. Dr. Razavian leads a broad spectrum of projects that span medical and industrial robotics, human motor neuroscience, and biomechanics. Dr. Razavian received his PhD (2018) and MASc (2012) in Systems Design Engineering from the University of Waterloo, and his BSc (2010) in Mechanical Engineering from Sharif University of Technology. Prior to joining NAU, he held an NSERC Postdoctoral Fellowship at Northeastern University, and a Postdoctoral Research Associate position at Imperial College London. Dr. Razavian is the recipient of multiple awards, including an NSERC Postdoctoral Fellowship Award (2019-2021), the Springer/IUTAM Lagrange Award for best thesis in multibody dynamics (2018), and the Society for the Neural Control of Movement Scholarship (2016).
Engagement-free and Continuous Health and Activity Monitoring via
Passive Seismic Sensing
Dr. WenZhen Song
October 30, 2023 | 9:00 am CST
This talk introduces our research on pervasive sensing for remote health monitoring. In response to the needs of continuous and engagement-free point of care, we invented a series of passive seismic sensing technologies to monitor human and animal health and activities. For example, BedDot, attached underneath a bed, senses seismograms from heartbeats and movements and estimates vital signs and activities. FloorDot, placed on a floor, senses seismograms generated by foot steps and other activities to identify the intrusions and activities of daily living (ADL). CageDot, placed under an animal cage, monitors animal activities and vital signs via analyzing the seismograms.
Dr. WenZhan Song is Georgia Power Mickey A. Brown Professor in Computer Engineering and Founding Director of the Center for Cyber-Physical Systems (CCPS) at the University of Georgia. He also holds the courtesy appointment in UGAcomputer science and statistics. He is a world leading expert on pervasive sensing, computing, networking and security and has created and deployed various innovative sensor network systems for health, energy, environment and security monitoring. Dr. Song’s research was featured in national media and received numerous awards and recognitions including NSF CAREER Award, Outstanding Research Contribution Award, Chancellor Research Excellence Award, IEEE Mark Weiser Best Paper Award, and three times Most Promising Technology awards from industry. Dr. Song served General Chair, TPC member and Associate Editor of the most prestigious conferences and journals at computer science and engineering, including IEEE INFOCOM, IEEE PERCOM, IEEE Internet of Things, ACM Transaction on Sensor Networks. His research has been funded by numerous grants from the NSF, NASA, USGS, DOE, USDA, NIH, DOD and industry.
Leveraging data-driven and physics-based approaches to develop predictive models for cardiovascular diseases
Transcatheter aortic valve replacement (TAVR) has become a standard therapeutic strategy for patients with symptomatic aortic stenosis. In 2019, TAVR was approved for low-risk patients by the U.S. FDA, and the number of TAVRs exceeded the number of surgical replacement operations. TAVR is associated with adverse outcomes and predicting them is an important step to optimize and improve pre-procedural planning and patient therapy. Using a combination of experimental, computational, and clinical data that correlate adverse outcomes with potential predisposing factors, we are working on the development of several predictive models and tools to assist clinicians with pre-procedural planning.
Dr. Hoda Hatoum
October 4, 2023 | 9:00 am CST
Dr. Hoda Hatoum obtained her BS degree in mechanical engineering from the American University of Beirut and her PhD degree from the Ohio State University (OSU). She was awarded the American Heart Association postdoctoral fellowship and right after graduation, she completed her postdoctoral training at the Ohio State University and at Georgia Institute of Technology. Her research lab at Michigan Tech focuses on tackling the complexity of: (a) structural heart biomechanics (adult and congenital); (b) patient-specific cardiovascular model development and in-vitro testing; (c) prosthetic heart valve engineering (surgical and transcatheter); (d) structure-function relationships of the heart in health and disease at the pediatric and adult stages; (e) turbulence in blood flow in relation to blood damage and (f) impact of arrhythmias and treatment approaches on cardiovascular flows. Dr. Hatoum has two patents filed on minimizing likelihood of leaflet thrombosis and on a novel implantable vascular shunt with real-time precise flow control. She received several recognitions including the Young Investigator Award at the Gordon Research Conference on Biomechanics in Vascular Biology and Disease 2023 and she was a finalist at the American College of Cardiology Young Investigator Award competition in 2021.
Wearable Acoustic & Vibration Sensing
and Machine Learning for Human Health & Performance
Dr. Omer Inan
November 30, 2022 | 9:00 am CST
Recent advances in digital health technologies are enabling biomedical researchers to reframe health optimization and disease treatment in a patient-specific, personalized manner. This talk will focus on my group’s research in three areas of relevance to digital health: (1) cardiogenic vibration sensing and analytics; (2) musculoskeletal sensing with joint acoustic emissions and bioimpedance; and (3) non-invasive neuromodulation for stress. Our group has extensively studied the timings and characteristics of cardiogenic vibration signals such as the ballistocardiogram and seismocardiogram, and applied these signals for cuffless blood pressure measurement, heart failure monitoring, and human performance. We have also leveraged miniature contact microphones to measure the sounds emitted by joints, such as the knees, in the context of movement, and have examined how these acoustic characteristics are altered by musculoskeletal injuries and disorders (e.g., arthritis). Finally, we have developed non-pharmacological treatment paradigms for posttraumatic stress disorder (PTSD) based on non-invasive vagal nerve stimulation, and have performed extensively validation of this approach with collaborators in psychiatry and radiology. We envision that these technologies can all contribute to improving patient care with lower cost.
Dr. Omer Inan is Professor and Linda J. and Mark C. Smith Chair in Bioscience and Bioengineering in the School of Electrical and Computer Engineering, and Adjunct Associate Professor in the Coulter Department of Biomedical Engineering, at Georgia Tech. He received his BS, MS, and PhD in Electrical Engineering from Stanford in 2004, 2005, and 2009, respectively. From 2009-2013, he was the Chief Engineer at Countryman Associates, Inc., a professional audio manufacturer of miniature microphones and high-end audio products for Broadway theaters, theme parks, and broadcast networks. His research focuses on non-invasive physiological sensing and modulation for human health and performance, and is funded by DARPA, NSF, ONR, NIH, CDC, and industry. He has published more than 300 technical articles in peer-reviewed international journals and conferences, and has twelve issued patents. He has received several major awards for his research including the NSF CAREER award, the ONR Young Investigator award, and the IEEE Sensors Council Early Career award. He also received an Academy Award for Technical Achievement from The Academy of Motion Picture Arts and Sciences (The Oscars). He is an Elected Fellow of the American Institute for Medical and Biological Engineering (AIMBE). While at Stanford as an undergraduate, he was the school record holder and a three-time NCAA All-American in the discus throw.
Intelligent Critical Care:
Opportunities & Challenges
Dr. Parisa Rashidi
October 28, 2022 | 9:00 am CST
In recent years, we have witnessed a rapid surge in building intelligent health systems. Artificial intelligence and machine learning techniques are central to all these systems and constitute a major step towards developing more intelligent healthcare solutions. These techniques not only make it possible to process and transform data into actionable knowledge, but also facilitate decision making and reasoning. This talk will discuss the rise of intelligent health systems in patient monitoring and will explore the challenges and opportunities in this area.
Dr. Parisa Rashidi is the foudning co-diretcor of the Intelligent Critical Care Center (IC3) at the University of Florida (UF) and an associate professor at the J. Crayton Pruitt Family Department of Biomedical Engineering (BME). She is also affiliated with the Electrical & Computer Engineering (ECE) and Computer & Information Science & Engineering (CISE) departments. Her research aims to bridge the gap between machine learning and patient care.
Dr. Rashidi is a National Science Foundation (NSF) CAREER awardee, the National Institute of Health (NIH) Trail Blazer Awardee, Herbert Wertheim College of Engineering Leadership Excellence Awardee, Herbert Wertheim College of Engineering Assistant Professor Excellence Awardee, and a recipient of the UF term professorship. She is also a recipient of UF’s Provost excellence award for assistant professors; with more than 500 tenure-track assistant professors at UF, Dr. Rashidi is one of only 10 to receive this award. She was invited by the National Academy of Engineering (NAE) as one of only 38 outstanding US engineers under 45 to participate in the EU-US Frontiers of Engineering (FOE) Meeting. To date, she has authored 170+ peer-reviewed publications. She has chaired six workshops and symposiums on intelligent health systems and has served on the program committee of 20+ conferences. Dr. Rashidi’s research has been supported by local, state, and federal grants, including awards from the National Institutes of Health (NIBIB, NCI, and NIGMS) and the National Science Foundation (NSF).
Population Coding
in the Cerebellum
A slow sensory system presents major problems for movement control. Yet, despite this shortcoming the healthy brain composes exquisite movements. Textbooks posit that this remarkable ability is due to the cerebellum, a structure that learns to predict sensory consequences, thus overcoming time delays. However, cerebellar neurons fire in patterns that do not correspond well with movements. Thus, the language with which the cerebellum expresses its predictions has remained a mystery. The idea that we have explored is that in the cerebellum, the fundamental unit of computation may not be a single neuron, but a group of neurons that share the same teacher. In this analogy, the teacher is the inferior olive, organizing the students (Purkinje cells) into groups. To test this idea, we have measured activity of neurons in macaques and marmosets and found that while activity of individual neurons is difficult to decipher, activity of a group of neurons that shares the same teacher is a rather precise predictor of the ongoing movement, particularly during deceleration and stopping.
Dr. Reza Shadmehr
September 30, 2022 | 9:00 am CST
Dr. Reza Shadmehr is a professor of biomedical engineering and neuroscience at the Johns Hopkins University School of Medicine. His research focuses on understanding how the human brain perceives the world, how it learns and how it controls our movements. Dr. Shadmehr also serves as co-director of the Biomedical Engineering Ph.D. program at Johns Hopkins University. Dr. Shadmehr received his undergraduate degree in electrical engineering from Gonzaga University. He earned a master’s degree in biomedical engineering and a Ph.D. in computer science (robotics) from the University of Southern California. Dr. Shadmehr completed the McDonnell-Pew post-doctoral fellowship at MIT and joined the Johns Hopkins faculty in 1995.
He has many published works including two books, The Computational Neurobiology of Reaching and Pointing and Biological Learning and Control. Dr. Shadmehr also has two patents filed.
Patient-specific Modeling
for Virtual Treatment Planning in Cardiovascular Disease
Dr. Alison Marsden
August 26, 2022 | 9:00 am CST
Dr. Alison Marsden is the Douglass M. and Nola Leishman Professor of Cardiovascular Disease in the Departments of Pediatrics, Bioengineering, and, by courtesy, Mechanical Engineering at Stanford University. She is a member of the Institute for Mathematical and Computational Engineering. From 2007-2015, she was a faculty member in Mechanical and Aerospace Engineering at UCSD. She graduated with a BSE degree in Mechanical Engineering from Princeton University in 1998, and a PhD in Mechanical Engineering from Stanford in 2005. She was a postdoctoral fellow at Stanford University in Bioengineering from 2005-07. She was the recipient of a Burroughs Wellcome Fund Career Award at the Scientific Interface in 2007, an NSF CAREER award in 2011. She was elected fellow of AIMBE and SIAM in 2018, the APS DFD in 2020, and BMES in 2021. Her research focuses on the development of numerical methods for cardiovascular blood flow simulation and application of engineering tools to impact patient care in cardiovascular surgery and congenital heart disease.
Cardiovascular disease is the leading cause of death worldwide, with nearly 1 in 4 deaths caused by heart disease alone. In children, congenital heart disease affects 1 in 100 infants, and is the leading cause of infant mortality in the US. Patient-specific modeling based on medical image data increasingly enables personalized medicine and individualized treatment planning in cardiovascular disease patients, providing key links between the mechanical environment and subsequent disease progression. We will discuss recent methodological advances in cardiovascular simulations, including (1) uncertainty quantification to assess reliability of simulation predictions, and (2) a unified finite element formulation for fluid structure interaction and fluid solid growth simulations. Clinical application of these methods will be demonstrated in two clinical applications: 1) virtual treatment planning in pediatric patients with peripheral pulmonary stenosis, and 2) prevention of vein graft failure after coronary bypass graft surgery. We will briefly discuss our open source SimVascular project, which is available to the scientific community (www.simvascular.org). Finally, we will provide an outlook on recent successes and challenges of translating personalized simulation tools to the clinic.
Seismocardiography
Genesis, and Utilization of Machine Learning for Variability Reduction and Improved Cardiac Health Monitoring
Seismocardiography (SCG) is the measured chest surface vibrations resulting from the cardiac activity. Although SCG can contain information that correlates with cardiac health, its utility is limited by a lack of understanding of the signal genesis and variability that can mask subtle SCG changes. Research presented in this talk address the genesis of SCG via Finite Element Modeling (FEM) of cardiac related chest vibrations and reduction of SCG variability using unsupervised machine learning (ML). The effects of cardio-pulmonary interactions on the SCG variability will be also analyzed. FEM analysis suggested that ventricular movement is a primary source of SCG. We will show that unsupervised ML helps reduce the SCG variability by clustering SCG beats into groups with minimal intra-cluster heterogeneity and unveiled consistent relations with the respiratory phases and SCG morphology. In our research, the longitudinal SCG measurements from reduced ejection fraction heart failure (rEFHF) patients were also utilized to predict patient readmission. Here, many time- and frequency-domain SCG features were extracted from clustered SCG beats. Using supervised ML, high classification accuracies were achieved suggesting high SCG utility for monitoring rEFHF patients and possibly other heart conditions.
Dr. Peshala Gamage
March 21, 2022 | 3:30 pm CST
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Dr. Peshala Gamage is an Assistant Professor in the Department of Biomedical and Chemical Engineering and Sciences at Florida Institute of Technology. Prior to his current role, he worked as a postdoctoral researcher in Florida Tech. He received his Ph.D. degree in Mechanical Engineering from the University of Central Florida in 2020 and received his M.Sc. degree in Aerospace Engineering from the same university in 2017. His current research interests lie in the areas of physiological signal processing, machine learning, biomedical acoustics, and computational modeling.
Daily Variations of
Brain Connectivity
Patterns:
A Graph-based Analysis
Dr. Farzad V. Farahani
April 25, 2022 | 3:30 pm CST
Dr. Farahani is a Postdoctoral Researcher in the Department of Biostatistics at Johns Hopkins University since Fall 2020. He earned his Ph.D. in the Computational Neuroergonomics track of the Industrial & Systems Engineering program at the University of Central Florida in 2020.
His primary research interest is to better understand the brain as a complex system in relation to behavioral performance, as well as how information is integrated across functionally specialized neural units that reside in spatially disparate brain regions. His current work focuses on analyzing connectivity patterns in the human brain using computational models such as graph theory and machine learning, as measured with functional and anatomical neuroimaging methods.
Most living organisms express a rhythmic cycle across a 24-hour period (circadian rhythm) that controls several physiological processes such as sleep–wake patterns, metabolic activity, and body temperature, as well as various brain functions such as attention, decision making, motor activity, and visual detection tasks. Furthermore, individuals have biologically different inclinations for when to sleep and when they are at their highest alertness and energy level, which are referred to as chronotypes. In this study, using graph-based knowledge and noninvasive imaging modalities such as functional MRI (fMRI), we examined the effect of both time of day and the individual’s chronotype on whole-brain network organization. The findings provide insight into daily variations in resting-brain networks, reflecting the universal effect of time-of-day on neural functional architecture when designing experiments. The findings also indicate the need to control for circadian typology, which could influence experimental results in neuroimaging studies.