Dr. Elizabeth Torres

Dr. Torres is a Computational Neuroscientist who has been working on theoretical and empirical aspects of sensory motor integration and human cognition since the late 90’s. She graduated from Mathematics and Computer Science and before joining the UCSD PhD program, she spent a year at the National Institute of Aging, NIH as a Pre-IRTA fellow, applying her skill set to the development of computational models for use in the field of geriatrics. Under the NIH-Pre-doctoral-fellowship, she completed graduate work with a focus on Applied mathematics to Neuro-Motor Control models in the field of Cognitive Science. She moved to CALTECH for postdoctoral training in electrophysiology and Computational Neural Systems, where she was a Sloan-Swartz Fellow, a Della Martin Fellow and a Neuroscience Scholar. She joined the Rutgers Psychology faculty, the Rutgers Center for Cognitive Science and the Center for Computational Biomedicine Imaging and Modelling of the Computer Science Dept., There she initiated the path of interdisciplinary collaboration and attained tenure. Her lab has brought to Rutgers over 8 Million research dollars from Federal, State and Private funds and multiple patents in the areas of digital biomarkers with applications to personalized mobile-Health. She has written two books on autism and biometrics, and currently serves as the Principal Investigator and Director of the New Jersey Autism Center of Excellence leading major initiatives to help transform autism medical research and treatments across the state.

Abstract

Autism is a neurological condition that requires complex medical care guided by scientific research methods. In current years, the need to develop physical outcome measures of treatment effectiveness has been propelled by the recognition of the high heterogeneity of the condition; the many subtypes of autism that fall under the same umbrella term; the lack of proper medical standards of current therapeutic methods -as expected by medical insurance companies, and the overall need to continuously assess this neurological lifelong condition as the person ages.

With shifting developmental priorities and new demands as the nervous systems age, it is evident that more accurate detection methods and tracking algorithms are required in autism research and clinical areas. The wearable sensors revolution and the new advances in AI, machine learning and data science make this time ripe for transformative change in all areas of medicine, with autism at the forefront of fields that could highly benefit from these new developments.

The NJACE, partly funded by the NJ Governor’s Council for the Medical Research and Treatments of Autism and the NJ Department of Health has assembled a consortium of top researchers from our Higher Education Research Institutions to adapt their ongoing research efforts in other medical areas to the areas of autism. Across our state, the commitment by our very own researchers to help ameliorate the consequences of NJ having the highest prevalence of autism nationwide, is commendable. 

We proudly present the regional research talents of New Jersey deploying top technologies at the service of Autism Medical Research and Treatments. Join us in a full day of interdisciplinary collaborative community effort to help improve the lives of those touched by this condition.

Dr. Dimitri Metaxas

Dr. Dimitris Metaxas is a Distinguished Professor of Computer Science Department at Rutgers University. He is director of the Center for Computational Biomedicine, Imaging and Modeling (CBIM) and the NSF I/UCR CARTA Center. Dr. Metaxas has been conducting research towards the development of formal methods to advance AI, computer vision, medical imaging and computer graphics. He has pioneered development of models for shape representation, deterministic and statistical object modeling and tracking, sparse learning methods, GANs and explainable AI. He has used these methods in pioneering work related to human emotions, deception, human motion estimation from motion capture data and his research was won many best paper awards. Dr. Metaxas has published over 700 research articles in these areas and has graduated 52 PhD students. He is a Fellow of the MICCAI Society, a Fellow the American Institute of Medical and Biological Engineers and a Fellow of IEEE.

Human Motion Understanding

Abstract

A central goal of artificial intelligence is to understand and reason about complex, real-world environments, human behaviors and their activities and enable reliable and efficient decision making. To address this problem we have been  developing a general, scalable, computational framework that combines principles of machine learning, sparse methods, mixed norms, AI, and deformable modeling methods. In this talk we will present new machine learning methods for
human behavior analytics and scene understanding which are capable of extracting facial features responsible for certain human behaviors, emotions, deception and we will present the use of GANs and self-supervised learning for applications such as monocular multi-view image generation, attribute editing and retargeting, and image to video translations for data augmentation and storytelling. Finally, will present studies on gait modeling and analytics that are invariant across gender, age and body dimensions and can help in the analysis of normal from patient gait.

Dr. Silvia Ortiz-Mantilla

Dr. Silvia Ortiz-Mantilla is a Research Assistant Professor at the Center for Molecular and Behavioral Neuroscience, Rutgers University-Newark. She received her MD degree and completed a dual residence program in pediatrics and pediatric neurology in her native country Colombia. During her 15 years of clinical practice, she had the opportunity to address a large range of neurological and developmental disorders.  After emigrating to the United States, she joined the Infancy Studies Laboratory at Rutgers University and under the direction of Dr. April A. Benasich, fulfilled a post-doctoral fellowship in developmental cognitive neuroscience moving thus, from clinical work to basic neuroscience research. Her current research focuses on early brain development, in particular on the neural mechanisms and oscillatory dynamics sub-serving native language acquisition in infants who are typically developing or are high-risk for developmental language disorders.  Dr. Ortiz-Mantilla’s approach uses converging methodologies such as dense-array EEG/ERP, source localization techniques, time frequency analysis, structural MRI measures, and behavioral assessments to further understand speech/language and perceptual processing across early development.

Early interactive acoustic experience: an intervention protocol to enhance pre-linguistic abilities in infants at familial risk for autism.

Abstract

Compromised language abilities and auditory processing deficits are ubiquitous in individuals with autism spectrum disorder (ASD). Due to its high heritability, ~20% of younger siblings of children diagnosed with ASD (FHA), will also develop ASD, showing language delays as early as 12 months of age. Furthermore, within the subset of FHA siblings not subsequently diagnosed with ASD, 10-38% will later meet diagnostic criteria for language delay. However, it is unclear whether the language deficits observed in children with ASD are causally related to deficient rapid auditory processing abilities and/or faulty establishment of phonemic mapping. Since the critical foundations of phonemic perception and phonemic mapping are established well before spoken language emerges, it seems evident that efforts oriented to improving language outcomes in FHA siblings should begin in early infancy. Intervention protocols using remediation techniques have not been employed with preverbal FHA infants. To heighten later language outcomes in infants at familial risk for developmental language disorders, including ASD, we have developed a baby-friendly intervention protocol that takes advantage of the plasticity characteristic of the developing infant brain to target key linguistic precursors over the period when the earliest foundations of language are being established. The intervention protocol uses an interactive acoustic experience with spectrotemporally-modulated non-speech stimuli, operant conditioning and infant-control algorithms that adaptively drive brain plasticity, provide support of prelinguistic acoustic mapping and enhance infant auditory processing speed and attention. This novel intervention has proved successful in typically developing infants and may well offer the promise of ameliorating or perhaps even preventing the disrupted language acquisition seen in ASD. 

Dr. Antje Ihlefeld

Antje Ihlefeld is on faculty in the Department of Biomedical Engineering at the New Jersey Institute of Technology. Originally trained in Cognitive and Neural Systems (Boston University) and Electrical Engineering (Dresden University of Technology), she is now directing the lab for Neural Engineering of Speech and Hearing. Her team studies auditory perception in tandem with cortical processing, using parallel methods in humans (functional near infrared spectroscopy) and gerbils (single units). This technology enables her to examine the mechanisms of hearing in situations with background sound. As a scientist, she is fascinated by this phenomenon and has devoted well over a decade of work to it. As an engineer, she believes that objective metrics of central auditory processing will be increasingly important clinically, for redesigning assistive listening devices such that they factor in individual differences in central auditory function.

Towards an objective scale of central auditory function

Abstract

Autistic people show a range of symptoms, including impaired sensory integration in the central nervous system. For instance, most autistic people have normal hearing as assessed through the sensory function of their ears. However, studies increasingly find comorbidity of central hearing loss and autism. This revelation hints why many autistic people struggle with verbal communication in everyday environments. Central hearing loss is known to impair hearing in noisy situations with background sound, such as restaurants or airports, a phenomenon called masking. Individuals with impaired central auditory function are particularly susceptible to a subtype of masking, called informational masking. At present, informational masking is identified operationally: when a target is expected to be audible, based on suprathreshold target/masker energy ratios, yet cannot be heard because perceptually similar background sound interferes. To predict and mitigate informational masking we need to develop a mechanistic framework for it. Using functional near infrared spectroscopy (fNIRS), we recently confirmed that sensory responses in ACx increase when a person listens actively to a target talker in the presence of informational masking (Zhang et al. 2018). The magnitude of this change, relative to passive listening, predicts speech intelligibility. Our new method shows promise for evaluating central auditory brain function in autistic people, with the ultimate goal of developing autism-appropriate screening tools for central hearing loss.

Dr. Ravi Ramachandran

Dr. Ravi P. Ramachandran is a Professor of Electrical and Computer Engineering at Rowan University. Dr. Ramachandran’s research interests are in 1-D and 2-D digital signal processing, digital filter design, speech processing, pattern recognition and biometrics. He received his Ph.D. from McGill University in 1990, M.S. in Electrical Engineering from McGill University in 1986 and the B.S. in Electrical Engineering from Concordia University in 1984. Dr. Ramachadran teaches/taught freshman and sophomore clinic, circuits, systems and control, digital signal processing, complex variables, architectures for digital signal processing, advanced digital signal processing, adaptive filters, biometrics and digital speech processing.

Promoting Access to Equitable Postsecondary Education Opportunities

Abstract

In this presentation, we will share a series of interdisciplinary research projects in process that center inclusion and communication access of students with autism/autistic students, with a particular focus on postsecondary education. The experiences of autistic college students will be shared along with a recommendation for interdisciplinary university collaborations. The researchers will discuss ways that technology does, or could, augment this work, while modeling an approach to research that centers the neurodiversity paradigm, the presumption of competence, and multimodal forms of communication. 

Dr. Amy Accardo

Dr. Amy Accardo is an Assistant Professor in the Department of Interdisciplinary and Inclusive Education at Rowan University. She is Director of the PhD in Education Program and teaches graduate students to address persistent social justice issues in education. Dr. Accardo’s scholarly activities focus on promoting equitable education opportunities for autistic students, and promoting inclusive educational practices through teacher preparation. Dr. Accardo presents at national and international conferences in the area of autism, access and equity.

Promoting Access to Equitable Postsecondary Education Opportunities

Abstract

In this presentation, we will share a series of interdisciplinary research projects in process that center inclusion and communication access of students with autism/autistic students, with a particular focus on postsecondary education. The experiences of autistic college students will be shared along with a recommendation for interdisciplinary university collaborations. The researchers will discuss ways that technology does, or could, augment this work, while modeling an approach to research that centers the neurodiversity paradigm, the presumption of competence, and multimodal forms of communication. 

Dr. Casey Woodfield

Dr. Casey Woodfield is an Assistant Professor in the Department of Interdisciplinary Education and Inclusive Education at Rowan University. Her research explores inclusive education, communication support partnerships, and stories of/through lived experiences of multimodal communication, interdependence and neurodiversity. Dr. Woodfield teaches courses on disability studies, inclusive education and autism, and serves as Professor-in-Residence in Rowan’s Professional Development School network. Her work aims to counter socially constructed notions of competence and voice, guided by the perspectives of disabled people as critical agents of change. 

Promoting Access to Equitable Postsecondary Education Opportunities

Abstract

In this presentation, we will share a series of interdisciplinary research projects in process that center inclusion and communication access of students with autism/autistic students, with a particular focus on postsecondary education. The experiences of autistic college students will be shared along with a recommendation for interdisciplinary university collaborations. The researchers will discuss ways that technology does, or could, augment this work, while modeling an approach to research that centers the neurodiversity paradigm, the presumption of competence, and multimodal forms of communication. 

Dr. Ravi Nataraj

Dr. Raviraj (Ravi) Nataraj is an Assistant Professor in Biomedical Engineering at the Stevens Institute of Technology. He received his PhD in Biomedical Engineering at Case Western Reserve University and his Master’s in Mechanical Engineering at Stanford University. His graduate work involved functional electrical stimulation (FES) with neural network control to restore standing balance following spinal cord injury (SCI). He has performed clinical research at the Cleveland VA Medical Center and the Cleveland Clinic working with individuals having SCI, carpal tunnel syndrome, and limb amputation. His research interests include training-platforms for better integration of individuals with movement disability to powered exoskeletons and neuroprostheses. His group is developing virtual reality platforms and instrumented wearables to co-facilitate greater cognitive agency for the user and optimal performance settings for the device. 

Virtual Reality and Instrumented Interfaces for Cognitive Soothing of Autism

Abstract

Virtual reality (VR) environments and flexible instrumentation allow for user-device interfaces to be highly customized to specific persons and conditions. In this talk, I will present technology that our laboratory is currently developing for user-device integration and propose how it may be leveraged for cognitive soothing in Autism Spectrum Disorder (ASD). Autism is marked by sensitivities in behavior, interaction, communication, and sensation. Specialized technological interfaces offer a potentially powerful platform to customize applications across multiple modes of consideration. Our lab has utilized visual and audio cues in virtual reality to observe how varying operation of an avatar can elucidate a positive link between agency, sense of control, and movement performance. We have also demonstrated in these environments how simple positive reward and tactile stimulation may co-enhance agency and performance. Our initial intention was to provide a basis for optimal training of users and adaptation of devices. However, the cognition-movement implications of our approaches may be broader. We propose that well-selected and well-placed sensory cues triggered by active engagement to a technological interface may produce ‘positive’ cognitive effects in ASD. The goal is to identify stimuli that are calming, soothing, and enjoyable and then effectively employ them through VR and tactile interfaces. Using VR and tactile interfaces that include low-cost force and flex sensors with remote (Bluetooth wireless) communication promotes broader clinical acceptance of these technologies. Applications can be well accessed using affordable technological components (computer, portable VR, light-weight accessories) that are easy to use with minimal set-up effort. Furthermore, cumbersome instrumentation may be especially unattractive for those in the ASD community. In this talk, we further discuss ways in which this technology can be packaged for streamlined applications.           

Dr. Ji Hye Ryu

Jihye Ryu is a postdoctoral researcher at the Sensory-Motor Integration Lab in the Psychology Department, Rutgers University and a member of the Rutgers University Center for Cognitive Science (RUCCS) in New Brunswick, NJ.

Dr. Ryu has devoted her career to the study of brain-body interactions and the development of clinically informed biometrics using digital data and validated observational clinical inventories. She is a finalist of the Innovation Fellowship Competition of Qualcomm Communications in 2016 and a recipient of the Fellowship of Excellence in Computational and Data Science by Rutgers Discovery Informatics Institute in 2018. Her unique background in finances, data science and cognitive neuroscience lends an innovative edge to her approach to contemporary issues in autism research.

Embodied Cognition Analytics Methods to Study Brain-Body Interactions while Aging with Autism

Abstract

Much research on Autism spectrum disorder (ASD) are focused on children with ASD. However, autism is a lifelong condition that requires different levels of support, as priorities shift across the lifespan. In our  xperience, the symptoms of aging adults with autism share some similarities with other geriatric disorders, as well as symptoms of their aging parents in some cases. Unfortunately, there is limited access to this population across the scientific community to study activities of the adults’ daily living. Moreover, very few use an objective means, as most clinical research rely on observation. However, with the recent advances in biosensors, it is now possible to digitize behaviors and study many clinical issues objectively within a mind-body interactive framework. This approach is bound to enrich our knowledge of autism as a systemic condition, and would allow intervening and measuring the effectiveness of treatment with objective outcome measures.

In this talk, I introduce the embodied cognition analytics (ECA) paradigm, which builds assays to characterize the neurological systems of adults with ASD in relation to other population groups. Specifically, this paradigm approaches autism holistically, by applying a set of personalized methods harnessing and analyzing biophysical signals from different functional levels of the nervous systems. These include the central, peripheral, and autonomic nervous systems, spanning different levels of functional control. We show results from different populations, including some with autism of genetic origins. We also discuss this new research program within the context of clinically informed digital biomarkers of naturalistic physical behaviors, amenable to assess autism across the lifespan. The methods are conducive to stratify different types of autism, and to study the departure of those aging with autism from typically aging.

Dr. Damiano Zanotto

Dr. Damiano Zanotto is an Assistant Professor of Mechanical Engineering at Stevens Institute of Technology (Hoboken, NJ), where he directs the Wearable Robotic Systems Laboratory. He received his B.S. and M.S. degrees (cum laude) in mechanical engineering and his Ph.D. in industrial engineering with a concentration in mechatronics, all from the University of Padua (Italy) in 2005, 2007, and 2011, respectively. Before joining Stevens, he was an Associate Research Scientist at Columbia University. Dr. Zanotto’s research interests include design and control of wearable robots for assistance and rehabilitation, wearable technology for motion analysis, and cable-driven parallel robots. His research has been supported by grants from NSF, Muscular Dystrophy Association, and New Jersey Health Foundation. Dr. Zanotto has authored or co-authored over 50 peer-reviewed journal and conference papers and holds two patents in the fields of wearable technology and rehabilitation robotics. He is a recipient of the Columbia University Translational Fellowship (2015). He currently serves in the IEEE-RAS Technical Committee on Mechanisms and Design. He also serves as an associate editor for Wearable Technologies (Cambridge University Press) and for several international conferences of the IEEE. 

Improving the Accuracy of Wearable Sensors for Gait Analysis Using Machine Learning Models

Abstract

The wearable technology field has grown an average of 16% year on year, in the last five years. New wearable sensors and related computational methods have been developed to measure biomechanical or physiological variables, with applications in human motion analysis and classification, diagnosis, injury prevention, human-machine interaction as well as virtual and augmented reality. While wearable inertial sensors have been proposed as alternatives to traditional laboratory equipment for low-cost and portable real-time gait analysis in unconstrained environments, modest reliability and validity currently limit their widespread use in research and clinical assessment.

In this talk, we will discuss how the vast expressive power of machine learning models can be leveraged to extract accurate kinematic and kinetic stride-to-stride gait parameters from noisy data measured by instrumented footwear during walking and running tasks. We will also show how these methods can be extended to estimate 3D joint angles during locomotion.  Results obtained in several studies involving young healthy adults, children with autism spectrum disorder, patients with spinal muscular atrophy, and elderly with vestibular disorders are presented to illustrate the feasibility of the proposed methods.

Dr. Peter Barrance

Peter Barrance, Ph.D., is a Senior Research Scientist at Kessler Foundation, a Clinical Research Scientist at Children’s Specialized Hospital, and a Research Associate Professor of Physical Medicine and Rehabilitation at Rutgers New Jersey Medical School. He directs the Musculoskeletal Biomotion Research Laboratory within the Center for Mobility and Rehabilitation Engineering Research at Kessler Foundation. Dr. Barrance received a bachelor’s degree in engineering science and a master’s degree in engineering mechanics at Iowa State University, and received research experience as a biomedical engineer in the Orthopaedic Biomechanics Laboratory at Johns Hopkins University. Following this, he pursued his doctoral degree in mechanical engineering at the University of Delaware, participating in interdisciplinary research that spanned engineering and rehabilitation science fields. In his dissertation work he developed and deployed a method to analyze knee joint motion in individuals with ligamentous injury using dynamic MRI. Following a two year post-doctoral fellowship at the University of Delaware, he joined Kessler Foundation in 2006. At Kessler Foundation he has continued to pursue research in disability with musculoskeletal origins, including a federally funded line of research on weight bearing imaging of the knee joint in individuals with knee joint osteoarthritis. This interest is complemented by an emphasis in pediatric mobility rehabilitation, in collaboration with physicians, physical therapists and researchers at Children’s Specialized Hospital. His research in pediatric mobility extends to the study of manual wheelchair propulsion patterns and energetics and the development of a gait retraining feedback system for children with cerebral palsy.

Wearable inertial sensing used in movement research in pediatric populations

Abstract

Inertial sensors, or inertial measurement units (IMUs), are wearable devices that can be used to measure body segment accelerations and movement patterns. As such, they present new opportunities for human movement research in settings outside the laboratory, for example within provider institutions and in the home. In pediatric rehabilitation research, these measurements can be used for description of movement, classification/detection of movement types, and therapeutic applications using biofeedback and serious gaming paradigms. The presentation will first briefly review operating principles, strengths and limitations, and validation findings for IMU based measurements. The experience of the speaker’s research team in using IMU systems to pursue two research lines in pediatric rehabilitation research will next be reviewed. The first is a descriptive project which compared movement patterns in wheelchair propulsion among pediatric manual wheelchair users. The second is a gait retraining application that provides biofeedback for children with hemiplegic cerebral palsy. The development course and preliminary research findings of this feedback training study will be reviewed, and the input received from physical therapy staff and the research participants themselves will be discussed. The final section of the presentation will review selected published works on movement description, classification/detection, and therapeutic intervention in populations with ASD. This review will include both those that have used inertial sensing and those that have used other technologies and may point the way toward additional areas in which inertial sensing can bring benefit.  The overall objectives of the presentation are therefore to inform the audience on the capabilities of these devices and to stimulate discussion of research opportunities in pursuit of the enhancement of quality of life and social integration for people with ASD.