Position: Ph.D. Candidate

Current Institution: Stanford University

Abstract:
Developing an Instrumented Mouthguard Sensor to Study Mild Traumatic Brain Injury

Mild traumatic brain injuries, commonly known as concussions, not only cause acute debilitating symptoms, but may also lead to long-term neurodegeneration. Due to heightened awareness of this problem, many companies and researchers have developed head impact sensors, some of which are advertised to quantify injury risks. Despite the hype, none of these sensors have been fully validated. In fact, a few sensors were shown to have >100% measurement errors. Thus, these sensors are far from ready as commercial injury risk predictors, and research data gathered using these sensors are questionable. A rigorously validated, accurate head impact sensor is needed. In our lab, we developed an instrumented mouthguard that is able to 1) capture head motion dynamics relevant to injury, 2) couple tightly to the skull for accurate measurement of skull kinematics, and 3) detect head impacts on the field with high sensitivity and specificity. Using human injury data gathered by this instrument, we found that brain deformation measures such as strain and strain rate may be better injury predictors than traditional skull acceleration measures. We also discovered that helmeted impacts on the field may be exciting a resonance of the brain and amplifying brain-skull relative motion. In addition, we studied brain injury risks in other activities, such as roller coaster rides, and found that they may lead to brain deformations on a similar level as mild sports impacts. In the near future, we hope to widely disseminate this technology to gather a large amount of human data for injury mechanism research. Once we know the link between the mechanical input and neurological deficit, we can further develop the sensor into a real-time injury screening device.

Bio:

I am a 4th year PhD student in the Bioengineering Department at Stanford University. In Dr.
David Camarillo’s Smart Biomedical Devices lab, my research focus is to develop a novel
instrumented mouthguard sensor to study mild traumatic brain injury. My anticipated graduation date is June 2017, and I would love to pursue a career in academia post-graduation.

I have always been passionate about biomedical research, since I am interested in electrical engineering and bioengineering, and feel strongly about doing research that may directly benefit healthcare. I completed my undergraduate education at the University of Toronto, majoring in Biomedical Engineering. In my second year, I worked in Dr. Yu Sun’s lab, developing a microfluidic system that aspirates cells to determine their mechanical properties. After year three, I interned at an MRI coil company called Sentinelle Medical as an electrical engineer, to develop imaging coils for breast cancer detection. For my undergraduate thesis, I learned and applied human factors engineering techniques to redesign the control interface for radiotherapy delivery systems. Through these experiences, I gained insight and developed skills in multiple aspects of medical devices research both in academia and in industry.

Coming to Stanford, I joined Dr. Camarillo’s lab, hoping to continue to apply my engineering
skills to better understand and solve healthcare problems. I came across the topic of traumatic
brain injury, and have strived to gain a better understanding of this ‘silent epidemic’ in my PhD
work. Upon joining the lab, I started developing an instrumented mouthguard device that contains inertial sensors to measure head motion during dangerous sports impacts. Using machine learning techniques, I developed a smart head impact classifier that can detect dangerous head motion on the field. Working closely with Stanford Athletics, I deployed instrumented mouthguards to the Stanford football team and collected a large human dataset. From this dataset, we identified potential mechanisms of concussion and promising injury risk predictors.

In my future research, I hope to continue to apply engineering techniques to study and improve human health. I plan to develop different wearable devices that can help gather data to characterize human diseases, and use data mining and machine learning approaches to analyze such data. I am especially interested in complex systems such as the brain, in relation to widely prevalent diseases including concussions and neurodegenerative disorders.

Being born in China and having spent most of my teenage years in Canada, I call many places home, including sunny California – where I am now. Aside from my research interests, I am also a passionate photographer. Just like in research, I use the camera to identify the beauty and balance in this world, and would love to continue exploring and making discoveries.