Edward Jay Wang

Ubiquitous Computing Researcher


TEDxSJI: What if we can live in a world where we can track our health like the way we do with the weather?

My name is Edward and currently I'm in my final year of my PhD at the University of Washington (fingers crossed). I work in the Ubiquitous Computing Lab advised by Shwetak Patel.

My research focuses on developing new sensing techniques for monitoring a person's health more continuously, conveniently, and cheaply, with a goal of ultimately bringing clinical sensing out of the clinic. With my expertise in mobile and embedded system prototyping, signal processing, and machine learning, I am actively creating new solutions in health monitoring. Through my PhD, I've transformed smartphones into medical devices without any hardware add-ons to screen for anemia and measure blood pressure, developed novel wearable devices that continuously track blood pressure and user context, and explored new ways to charge wearable devices right through the body. The next generation of medical sensing needs to leave the confines of labs and clinics, and be truly usable by everyone. And this has to start at even the earliest prototypes, something I strive for in all of my work. I have tested technologies in patient rooms, performed in-the-wild studies where users take our prototypes home, and even partnered with various NGOs to perform true user testing in places like the Peruvian jungle. 

Research Topics


The Next Billion Medical Devices

Today's medical devices are typically centralized in high-resource places like hospitals and cities, leaving a large portion of the world like low-income regions, rural areas, and chronic at home care scenarios under serviced. The proliferation of smartphones present an interesting opportunity to solve this issue of global access to medical screening and management. Smartphones have a variety of sensors built in to them, and by tapping into these sensors through software augmentations, I've worked on solutions to transform the billions of smartphone into a medical device, with a simple app download.

HemaApp - Noninvasive hemoglobin measurement using a smartphone camera and flash LED. The camera measures the change in spectral absorption distribution to determine the concentration of hemoglobin in the blood. The user places their finger over the camera and flash LED, allowing the camera to capture the blood movement in the finger. The blood movement caused by the heart beat provides a signal source for non-invasively extracting the absorption contribution by blood against the baseline absorption of skin, muscle, and bone. 

Through the development of this work, I've done multiple rounds of in-clinic studies with UW Medical Center, Seattle Children's Hospital, and is gearing up for new large scale and longitudinal studies with Harborview Medical Center and the Fred Hutch. Not only have I tested HemaApp in clinical settings, I've worked with NGOs in Peru to test the concept on ground zero. Community health workers used our app during an anemia screening campaign in the Amazonian jungle. 

Seismo - Blood pressure monitoring using a smartphone camera and accelerometer. An alternative method to measuring blood pressure besides the use of a blood pressure cuff is a method called pulse transit time (PTT). PTT is the time it takes for the pressure pulse generated by the heart to travel a set distance on the artery. Seismo, utilizes the smartphone accelerometer to pick up the acceleration created by the aortic valve opening as a timing marker for the genesis of the pressure pulse. With the phone camera, the ensuing pressure pulse is captured down stream as it arrives at the finger tip. 



New Medical Sensing Opportunities with Wearable Devices

The current clinical paradigm relies on active measurement taking using a medical device. May it be blood pressure measurement, glucose monitoring, blood testing, oral hygiene, etc, our medical diagnostics rely on physiological metrics that are a single measurement in time. However, with the advent of consumer wearable devices, more and more attention is being put on the possibility of doing more continuous monitoring of physiological metrics. The first of such is continuous heart rate monitoring, overall activity level, and sleep quality through movement analysis. In my research, I have worked on a variety of aspects to push the boundaries of continuous monitoring, ranging from new physiological monitoring techniques to solutions to charge wearable devices in a passive way.

Glabella is a device that integrates multiple optical sensors into a pair of glasses to measure blood pressure continuously using PTT. The PTT is measured using optical sensors placed strategically along the frame of a pair of glasses. When the user wears the glasses, the optical sensor captures the arrival time of the pulse at different parts of the facial arteries. Such a solution can be integrated into any head-mounted wearable device. 



Contextual Understanding of the USER

Beyond direct physiological monitoring, one of the aspects that is needed for a full understanding of the human health is the activity and contextual information the person is in. In some ways, this is the outside influence and the physiological metric is the response. Being able to capture information about what a person is doing through out the day can ultimately provide us opportunities to understand how our behaviors are related to our health, or even improve our sensing capability by using our current context to adjust the sensing. 

MagnifiSense  is a wrist-worn magnetic sensing technique to capture what a person is doing by detecting the electronic environment they are current in the presence of. Most electronics have a unique electromagnetic radiation due to the internal electronics such as motors, power switches, processors, heating elements, etc. By knowing what someone is using, for example turned on the stove, it is possible to infer someone is cooking. If someone is driving vs riding a bus vs biking (the lack of EMI), the EMI footprint is different, providing information about commute patterns. The EMI based classification of activity patterns provides a rich set of information while maintaining a fairly low power requirement as compared to vision based systems, and only requires a wrist-worn device versus needing to instrument the environment. 

Carpacio is a system that takes advantage of the capacitive coupling created between the touchscreen and the electrode present in the seat when the user touches the capacitive screen. Using this capacitive coupling phenomenon, a car infotainment system can intelligently distinguish who is interacting with the screen seamlessly, and adjust its user interface accordingly. Through this method, it is possible to examine subtle contextual information such as driver attention for future automotive systems. 



CASPER: Capacitive Serendipitous Power Transfer for Through-Body Charging of Multiple Wearable Devices. 
Edward J. Wang, Manuja Sharma, Yiran Zhao, and Shwetak N. Patel. 
ISWC '18 | To Appear

Seismo: Blood Pressure Monitoring using Built-in Smartphone Accelerometer and Camera.
Edward J. Wang, Junyi Zhu, Mohit Jain, Tien-Jui Lee, Elliot Saba, Lama Nachman, and Shwetak Patel.
CHI '18 | PDF
Honorable Mention (Top 5%)


Carpacio: Repurposing Capacitive Sensors to Distinguish Driver and Passenger Touches on In-vehicle Screens
Edward J. Wang, Jake Garrison, Eric Whitmire, Mayank Goel, and Shwetak Patel
UIST '17 | PDF

Glabella: Continuously Sensing Blood Pressure Behavior using an Unobtrusive Wearable Device. 
Christian Holz and Edward J. Wang.
PACM  on Interactive Mobile, Wearable and Ubiquitous Technologies (IMWUT) 1, 3, Article 5 (September 2017) | PDF

Noninvasive Hemoglobin Measurement using Unmodified Smartphone Camera and White Flash
Edward J. Wang, William Li, Junyi Zhu, Rajneil Rana and Shwetak N. Patel
EMBC 2017 | PDF


HemaApp: Noninvasive Blood Screening of Hemoglobin using Smartphone Cameras.
Edward J. Wang, William Li, Doug Hawkins, Terry Gernsheimer, Colette Norby-Slycord, and Shwetak N. Patel.
Ubicomp '16 | PDF
Best Paper Award (Top 1% of submitted papers)

A Smartphone-based System for Assessing Intraocular Pressure
Alex Mariakakis, Edward J. Wang, Shwetak Patel and Joanne C. Wen
EMBC 2016 | PDF


MagnifiSense: Inferring Device Interaction using Wrist-worn Passive Magneto-inductive Sensors.
Edward J. Wang, Tien-Jui Lee, Alex Mariakakis, Mayank Goel, Sidhant Gupta, and Shwetak N. Patel
UbiComp '15 | PDF

Skin Drag Displays: Dragging a Physical Tactor across the User's Skin Produces a Stronger Tactile Stimulus than Vibrotactile.
Alexandra Ion, Edward J. Wang, and Patrick Baudisch.
CHI '15 | PDF


Design Considerations for Leveraging Over-familiar Items for Elderly Health Monitors
Edward Wang, Samantha Ipser, Patrick Little, Noah Duncan, Benjamin Liu, Shinsaku Nakamura.

Teaching & Course Development

I have had the opportunity to work with students from all levels through the courses that I develop. My teaching philosophy is that the best way to learn is to build it, break it, and have fun with it. Leave room for creativity and definitely challenge the students enough so that they crash and burn a few times. The classes I design motivate students to explore the world of embedded systems, signal processing, and interaction by directly using these concepts in projects that are designed to provide the students with the skills and understanding to be able to take on real world problems beyond the course. 


Ubiquitous Computing

UW Electrical Engineering Professional Masters Program

I am a co-course developer with Alex Mariakakis for the Ubiquitous Computing course for the UW EE PMP course offering in Spring 2018. The course is centered around giving professional masters students already working in industry companies such as Microsoft, Boeing, and Amazon to become acquainted with the hot topics in technology. As such, each time this course is offered, the content is revamped to reflect the newest and the coolest in technology. This year, we are tailoring the course to introduce students to Android programming, machine learning, signal processing, embedded device programming, Rapid 3D prototyping, AR and VR interactions, and Edge Computing. The course is centered around a project that builds upon itself through the quarter, with the final creation being an embedded system encapsulated in a 3D printed housing that dynamically interacts with an Android program performing on-board machine learning and signal processing.

Introduction to Device Programming

EDx (Microsoft Course) [DEV295x]

I helped design the first of a series of online edX courses on IoT devices offered by Microsoft. In this class, we introduce students to basic concepts in sensors, embedded device, C programming, and finally connecting their first IoT device to the Azure cloud. For the course, each student used a RaspberryPi 3 to interface with a variety of analog and digital sensors in the Microsoft IoT Pack for Raspberry Pi 3 sold at Adafruit. 

Biosignals Processing

HMC Undergraduate Sophomore Lab

The Biosignals Processing Lab is a course that I lead the development of in my Junior to Senior year summer at Harvey Mudd College, and subsequently taught in Fall 2011 and Fall 2012. I was responsible for deciding the material the students will learn and developing the corresponding material such as pre-lab material to teach core MATLAB skills or conceptual readings on physiology. Through the course of a summer I came up with 4 experiments involving the eye (EOG), muscle (EMG), heart (ECG), and the brain (EEG), each designed to teach the student a different concept of signal processing. In each of the experiments, the students were introduced to the physiological basis for each signal in their pre-labs and during their in-lab sessions, students were presented with a series of questions such as "how do the placement of electrodes affect the signal quality" and "how does the frequency content of the EMG change with respect to fatigue." The students were not given the experimental protocol, but instead were to draft their own experimental procedure in order to answer these questions.