Wearables for autism and aphasia after stroke
We welcome you to join us in-person and on Zoom for our December eWEAR Seminar.
Date: Monday, December 12th from 12:30 pm to 1:30 pm PDT
Location: Stanford University (Y2E2 Building, Room 299) & on Zoom
Lunch will be provided at 12:00pm for in-person attendees & a chance to talk with the speakers after the seminar.
Registration: Please click here to register
Safety Protocol: For visitors coming to campus please review the Stanford University Covid-19 Policies. Face coverings are strongly recommended for everyone attending.
12:30 pm to 1:00 pm
“Slow paced breathing pacer device designed for children diagnosed with autism spectrum disorder”
Maheen Mausoof Adamson
1:00 pm to 1:30 pm
“Voice to the speechless”
Postdoctoral Research Fellow, Psychology, Stanford University
Pardis Miri has a PhD in computer science in the area of human computer interaction and currently is a postdoctoral fellow at Stanford University where she has created the Wehab lab. At Wehab, she leads a multidisciplinary research team aimed at the design, engineering, and evaluation of technologies to help people to manage their mental and physical well being. She works at the intersection of human computer interaction and affective science. Through her multidisciplinary career as a postdoc, she has collaborated with and brought together many academics and mentored over 30 students. She is entering the job market this year and is looking for future collaboration opportunities.
FAR is a personalizable haptic breathing pacer system prototype with a companion app designed for children diagnosed with autism spectrum disorder (ASD) to manage their problem behaviors in the moment. To the best of our knowledge, FAR is the first fully functional vibrotactile system designed for ASD children that withstood usability testing in vivo for two weeks.
In this talk I’ll do three things.
First, I’ll give a brief systems description of how the FAR system works and what major use cases we are considering for it. Then, I’ll describe my observations, gleaned from personally being involved in design, engineering and evaluating FAR, about some of the technical and social challenges, why I feel they are important, and how we addressed or aimed at addressing them. Finally, I’ll make observations about how we think about taking this prototype into the market, and see if you agree that this is a journey worth taking.
Maheen Mausoof Adamson
Clinical Associate Professor (Affiliated), Neurosurgery, Stanford University, School of Medicine
Dr. Maheen Mausoof Adamson is a clinical associate professor of Neurosurgery (Affiliated) at Stanford School of Medicine, Research Director for a national Women’s Operational Military Exposure Network (WOMEN) and Senior Scientist for Rehabilitation Services at VA Palo Alto. Adamson completed her undergraduate degrees in neurobiology and women studies at the University of California, Irvine. She completed her Ph.D. in neuroscience from the University of Southern California and a postdoctoral fellowship in Psychiatry and Behavioral Sciences at Stanford School of Medicine. She has recently finished her Masters in Healthcare Leadership from Brown University and Faculty Innovation Fellowship at Byers Biodesign Fellowship at Stanford University School of Business.
Dr. Adamson’s expertise and interests span employing translational neuroscience methodologies for diagnostic and neuromodulation treatments for frequent health problems in patients with Traumatic Brain Injury (TBI), psychiatric problems, and Alzheimer’s disease. She has employed advanced structural and functional imaging modalities and biomarker assessments in Veteran, active military and civilian populations with these health problems. She has been a leader in identifying gender differences in brain injury, particularly in the Veteran population. She currently serves as PI and Site-PI on numerous neuromodulation clinical trials and collaborates internationally for developing advanced diagnostic methods in neuroimaging, especially in underserved communities. In her position at VA Palo Alto, she is actively involved in translating research, such as non-invasive brain stimulation and other therapies, to clinical in-home use by patients using innovations such as virtual and augmented reality.
Dr. Adamson has authored numerous peer-reviewed publications on the cognitive and neural basis of Alzheimer’s disease and on a wide range of topics in TBI. She has received recognition in national and international settings. She is also intricately involved in mentoring research postdoctoral fellows and clinical residents in Physical Medicine & Rehabilitation, Psychiatry and Neurosurgery departments at Stanford School of Medicine. Her goal is to incorporate advanced treatment and diagnostics tailored to each patient’s needs into standard-of-care to improve their daily function, reintegration into society and long-term rehabilitation.
The combined impact of communication loss, depression and risk of the next neurological impact is deleterious to the quality of life of older adults, their caregivers, and the healthcare system. Currently, there are no comprehensive solutions that target older adults with communication disorders resulting from injuries that involve their caregivers and providers with monitoring, assessments, and alerts for increase in depression or indications for other neurological events. Soof Solutions, Inc. proposes to develop a comprehensive communication-detection method using eye tracking technology, speech therapy stimuli, used currently in a clinical setting, standardized assessment tools for depression, anxiety, sleep, cognitive decline, and fatigue, and a patented artificial intelligence algorithm based on eye tracking metrics. Because communication or language loss may also be accompanied by loss of motor control in this population, this method must include (in addition to voice commands and touch screen) a non-speech and non-limb-based communication method such as eye tracking, that is both accurate, efficient and easily monitored with measurable outcomes (e.g., reduced depression). The eye tracking metrics will also serve as the database on which an algorithm will be developed to monitor and detect the variability in eye movements along with physiological monitoring to detect early signs of repeat neurological insults such as a stroke.