Edward Jay Wang, PhD

Researcher / Educator/ Entrepreneur

Assistant Professor UCSD ECE & Design Lab

CEO & Founder Billion Labs Inc.

CTO & Founder Motion Minder Inc.

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

Research Talk: The Next Billion Medical Devices

HCII Seminar: Elevating Access to Remote Health Monitoring Fairly

I am a Tenure Track Faculty at UCSD’s Electrical and Computer Engineering department and the Design Lab as an Assistant Professor. I am the PI of the UCSD Digital Health Technologies Lab. I am affiliated with the CSE department, Center for Wireless and Population Health Systems (CWPHS), Center for Wearable Systems (CWS), and serve as a Board of Director for the Center for Mental Health Technology (MHTech). I am also the Founder of two digital health companies born out of technologies developed in my academic laboratory: Billion Labs Inc. and Motion Minder Inc.

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. I am actively creating new solutions in health monitoring with my expertise in mobile and embedded system prototyping, signal processing, machine learning, and a strong command of medical knowledge. 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 to be truly usable by everyone. This has to start at even the earliest prototypes, something I strive for in all of my work. Towards this goal, 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.

The work in my group is made possible by generous funding from the National Institute of Health, National Science Foundation, and Google.

Contact me at: ejaywang {at} ucsd {dot} edu

Publications

2023

A Calibration Method for Smartphone Camera Photophlethysmography
Yinan Xuan, Colin Barry, Nick Antipa and Edward Jay Wang
Frontiers in Digital Health

Racially Fair Pupillometry Measurements for RGB smartphone cameras using the Far Red Spectrum
Colin Barry and Edward Jay Wang
Scientific Reports

Ultra-low-cost mechanical smartphone attachment for no-calibration blood pressure measurement
Yinan Xuan, Colin Barry, Jessica De Souza, Jessica H Wen, Nick Antipa, Alison A Moore, Edward J Wang
Scientific Reports

Neuromorphic High-Frequency 3D Dancing Pose Estimation in Dynamic Environment
Zhongyang Zhang, Kaidong Chai, Haowen Yu, Ramzi Majaj, Francesca Walsh, Edward Wang, Upal Mahbub, Hava Siegelmann, Donghyun Kim, Tauhidur Rahman
Neurocomputing

Detecting Periodic Biases in Wearable-Based Illness Detection Models
Amit Klein, Varun Kumar Viswanath, Benjamin Smarr, Edward Jay Wang
ICLR 2023 Workshop on Time Series Representation Learning for Health

Investigating interactive methods in remote chestfeeding support for lactation consulting professionals in Brazil
Jessica de Souza, Cinthia Calsinski, Kristina Chamberlain, Franceli Cibrian, Edward Jay Wang
Frontiers in Digital Health

2022

Smartphone camera oximetry in an induced hypoxemia study
Jason S Hoffman, Varun K Viswanath, Caiwei Tian, Xinyi Ding, Matthew J Thompson, Eric C Larson, Shwetak N Patel, Edward J Wang
npj Digital Medicine

At-Home Pupillometry using Smartphone Facial Identification Cameras
Colin Barry, Jessica De Souza, Yinan Xuan, Jason Holden, Eric Granholm, Edward Jay Wang
CHI 22 Best Paper Runner Up

Opportunities in designing HCI tools for lactation consulting professionals
Jessica De Souza, Kristina Chamberlain, Sidhant Gupta, Yang Gao, Nabil Alshurafa, Edward Jay Wang
CHI EA ‘20

SIG: Towards More Personal Health Sensing
Junyi Zhu, Liang He, Jun Nishida, Hamid Ghaednia, Cindy Hsin-Liu Kao, Jon E Froehlich, Edward Jay Wang, Stefanie Mueller
CHI ‘20 Special Interest Group (Organizer)

2021

Tenets towards smartphone-based medical tricorders
Colin Barry, Tauhidur Rahman, Edward J Wang
Mobisys ‘21 DigiBiom Workshop (Organizer)

2020

Multi-Channel Facial Photoplethysmography Sensing
Parker S Ruth, Jerry Cao, Millicent Li, Jacob E Sunshine, Edward J Wang, Shwetak N Patel
EMBC '20

2019

Challenges in Realizing Smartphone-Based Health Sensing
Alex Mariakakis, Edward J. Wang, Shwetak Patel, Mayank Goel.
IEEE Pervasive Computing Volume: 18, Issue: 2, April-June 1 2019| PDF
Best Paper Runner Up

2018

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 | PDF

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
Best Paper Honorable Mention (Top 5%)

2017

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
Distinguished Paper Award

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

2016

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%)

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

2015

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

2013

Design Considerations for Leveraging Over-familiar Items for Elderly Health Monitors
Edward Wang, Samantha Ipser, Patrick Little, Noah Duncan, Benjamin Liu, Shinsaku Nakamura.
HCII'13 | PDF

Research Themes

My research aims to enable a world where clinical care is distributed and available beyond the clinical settings, asking the question of how we can provide the sensing infrastructure to enable wide spread physiological monitoring and disease discovery. I have approached this in two major directions: (1) Bootstrapping the next billion medical devices with smartphone sensors, and (2) Passive and continuous monitoring of health and activity using next generation wearable devices.

Bootstrapping 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.

Widely Distributable Hypertension Screening: BPClip - An ultralow cost attachment constructed purely out of plastic housing and a spring transforms a smartphone into a blood pressure monitor. We take advantage of a pinhole camera effect to convert the smartphone camera into a pressure sensor, effectively allowing the camera to measure simultaneously the pressure applied to the finger’s digital artery, and the pulse amplitude. In essence, providing the camera the ability to measure the force needed to cut off the flow. This is the same underlying principle as an upper arm oscillometric BP measurement, requiring no per-user calibration. Making this a viable, low cost solution for massively screening for hypertension. Our vision: a blood pressure monitor can be handed out to whoever needs one the same way a toothbrush is by a dentist.

On-the-go Blood Pressure Tracking: 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. Although this method requires calibration, it provides a good estimate of changes in one’s blood pressure over time.

Global Anemia Screening: 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.

Passive and continuous monitoring of health and activity

A new generation of medical devices will take the form of “everyday” things like clothing, glasses, watches, tattoos and maybe even tooth fillings. These devices will act more like the way medical devices are used in the hospital room, continuous and in the background, constantly providing insights, used in testing hypothesis about a person’s conditions; all of this being done everyday when the person may or may not be sick. We are interested in taking a data science approach to (1) develop new populational/physiological insights through examining large-scale wearable data and (2) design future wearable devices.

Uncovering Hidden Biases in Wearable Illness Detection: Wearable health devices have revolutionized our ability to continuously analyze human behavior and build longitudinal statistical models around illness by measuring physiological indicators like heart rate over several months of an individual's life. Shifts in these indicators have been correlated with the onset of illnesses such as COVID-19, leading to the development of Wearable-Based Illness Detection (W-BID) models that aim to detect the onset of illness. While W-BID models accurately detect illness, they often over-predict illness during healthy time periods due to variance caused by seemingly random human choices. However, it is because W-BID models treat each input window as independent and identically distributed samples that we are unable to account for the weekly structure of variance that causes false positives. Towards preventing this, we propose a system for identifying structural variance in wearable signals and measuring the effect they have on W-BID models. We demonstrate how a simple statistical model that does not account for weekly structure is strongly biased by weekly structure

Detecting Acute Illness in Natural Longitudinal Users: Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification.

Continuous BP Sensing: 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.

Low Power Activity Tracking: 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.

Through-body Device Charging: CASPER is a wearable device charging solution that uses the body as a conductive element for a 13.56MHz AC capacitive charger to charge devices worn on the user’s body. The purpose of CASPER is to allow charging of wearables without requiring frequent removal or human intervention. CASPER is a capacitive charging system that can be embedded in beds, seats, and other common furniture so that serendipitous daily contacts can facilitate opportunistic AC power circuits through the body to trickle charge body-worn devices. Using our charging system, we designed a wound monitoring gauze pad that charges each night in bed and monitors the patient’s wound with optical pH sensing and capacitive wetness sensing to indicate whether it is time to replace the gauze pad. This e-bandage design can recharge each night whenever the patient is in bed without ever needing to be removed.

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.

Mobile Health Device Design

Graduate level class introducing students to the vast array of solutions being targeted in the mHealth domain through a set of curated survey readings. The student will also get hands-on experience learning how to prototype both hardware (Arduino) and software (Python with Scipy) of a few example mHealth devices and standard evaluation techniques used in research for analyzing the performance of the system. Finally, as important as it is to learn how to build and test is the practice of ideation. In a student group, the student will engage in the proposal, design, build, and testing of their own mHealth system. Students should be proficient in programming and basic knowledge of data processing.

Introduction to Engineering Design

In a course focused on designing useful and meaningful solutions, this undergraduate level hands-on course teaches students how to work with a customer to build a tangible solution. Students are paired with fellow student customer from the class. Rather than making something that suits their own interests, students learn, often for the first time, how to work with a customer. To understand their needs, work with them to iterate on potential solutions, and ultimately build and test their solution with the customer. In this class, students learn the basics of embedded device programming, Rapid 3D prototyping, Edge Computing, and signal processing. But in the lens where they acquire new skills needed to achieve the needs of the customer. On top of technical skills, students learn how to apply design methods and analysis such as the Kano Method to assess customer needs, and design with Affordance in mind.

Biosignals Processing

The Biosignals Processing Lab is a laboratory course that introduces students to the biophysics of biosignals, the acquisition/processing of biosignals, and applications of bio signals. The course uses a mix of conceptual readings on physiology and lab experiments. Students learn about 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 are 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.

Design @ Large 20’/21’

Racism in the Design of Everyday Things

Racism is deeply rooted in all facets of society as well as all other “-isms”. Everything we use, big and small, is ultimately designed. Whether intentional or not, conscious or not, the design of these everyday things is shaped by the cultural backing of those who design it and the societal context in which it is designed. That means that racism is built into design; therefore everyone engaging in design must understand the historical context of racism. Design is a way of thinking: addressing the core issues, always taking a systems point of view, emphasizing the role of people in the complex systems of the modern world, and continually iterating on our work. In order to design equitably then, we need to not only meaningfully engage with various stakeholders, but we must also understand how racism manifests in society so that we are able to see how it permeates our design and design processes. What may seem “typical” or “neutral” is actually the product of decisions interlinked through historical contexts, biases, and trials of oppression. Without building our capacity to understand these historical contexts, we aren’t able to see racism in everyday things and therefore perpetuate it.

In partnership with Carrie Sawyer, founder of Diversity by Design, we have developed this quarter’s series as a set of talks that explore a few broad topic areas. Each topic area consists of talks that will help shed light on the historical context of racism and the consequences of “designing” without understanding racism’s deep roots as well as provide examples of anti-racist and equitable approaches in practice across various domains. We have chosen to offer “suggested pairs” of talks that complement one another and help to showcase the need to continually build our own capacity. Too often we want to jump straight to action, but without understanding the historical context of racism (and other “isms”), we perpetuate racism and inequality - even with the best intentions.