A large portion of my work focuses on understanding the opportune moment for mental health intervention, and developing wearable-based intervention for conditions such as substance use and social isolation.
CravingMitigator: Designing Minimally Obtrusive Arousal Reduction as Craving Countermeasure
Addiction-related disease claims the lives of millions every year, and in addition causes great morbidity and disrupts many lives. Cravings for addictive substances due to chemical withdrawal or psychological factors make it difficult to maintain abstinence and successfully reduce or quit using the addictive substance. Existing digital methods used to help people overcome cravings in the moment involves either interacting with mobile application, text messaging, or utilize relaxation breathing. These methods may be obtrusive and thus not viable for use in all locales or situations and thus a less obtrusive method of combating cravings warrants research. A minimally obtrusive intervention could help people without taking them away from their immediate surroundings, allowing them to avoid retreating from work or social situations. Prior research by our group on arousal management has shown that offset heartbeat signals delivered through haptic feedback can reduce or increase heart rate and thus influence autonomic arousal. In this study, we aim to characterize whether offset heartbeat signals can mitigate the autonomic arousal induced by craving, and whether such intervention can affect the perceived level of craving.
This work is in partner with Click Therapeutics.
PuffSensing: Predicting Craving and Using of Electronic Nicotine Delivery System (ENDS) with On-Body and Mobile Sensing for Young Adults
The use of e-cigarette, especially in younger population, is an increasing concern in United States. Based on the previously developed PuffPacket, an attachment to measure the usage of e-cigarette, we are conducting a study to explore how to predict when people use e-cigarette, and what signals are informative about the craving for e-cigarette. In addition to PuffPacket, we are developing a mobile sensing application to identify the lifestyle of the user, and to collect the user's reflection of their vaping experience. We are deploying this study with a young adults population in urban area. We hope the findings of this study can help us predict the opportune moment for giving interventions to reduce or control e-cigarette usage.
I also develop on-skin devices for delivering social support, as well as distributed charging for on-skin devices.
Skincelet: Delivering Social-Physical Interaction Through On-Skin Device
Social-physical interaction such as handshaking, handholding and hugging is an essential part of how human communicate, collaboration and interact with the world around them. Existing devices have explored ways to simulate social-physical interactions through wearable devices, ranging from clothes that hugs the wearer, to wearable devices that deliver touching and stroking using vibrotactile actuation, gas chamber or mechanical interaction. This project aims to explore novel ways to deliver social-physical interaction, leveraging on existing accessories with social significance that people already wearing. We chose to develop on-skin devices because they are close to skin, non-obtrusive to wear, and can create organic, human-like impression rather than machine-like impression. We are developing Skincelet, a secondary-skin actuation device that deliver hand-based social-physical interaction. Ultimately, we aim to answer how we can design systems to deliver social-support remotely and non-obtrusively as a mental health intervention.
CASPER: Capacitive Serendipitous Power Transfer for Through-Body Charging of Multiple Wearable Devices
A charging solution that augment everyday objects such as beds, seats, and frequently worn clothing to provide convenient charging base stations that will charge devices on our body serendipitously. We performed an extensive parameter characterization for through-body power transfer, present a design trade-off visualization, and demonstrated how we utilized this design process in the development of our own smart bandage device and a LED adorned temporary tattoo that charges at hundreds of micro-watts using our system.
Published in Ubicomp 2019
I aim to help patients advocate for their preferences and needs in healthcare.
Supporting collaborative goal-setting for hospitalized adolescent patients
To increase patient engagement and facilitate patient-provider collaboration, tools that incorporate patients’ goals into medical care plans are needed. However, few studies have explored how hospitalized patients set and share goals to communicate with their caregivers and clinical care teams. Even less is known for how pediatric patients experience sharing their goals during hospitalization. This paper presents a technology probe study to characterize how pediatric patients perceive goal-setting, and how goal-sharing affects their collaboration with their caregivers and clinical care teams. We conducted this study with 13 patient families and 4 clinicians. We found that goals set and shared by pediatric patients foster the patients’ autonomy to participate in care decision-making, reveal the gaps of understanding between patients and caregivers, support the patients emotionally during patient and care team interaction, and convey the patients’ personalities and preferences to the clinical care team. In addition, we recommend design opportunities to support the different ways that patients’ goals can foster high-quality patient care. We also discuss how patients’ goals impact the tension of shared decisional authority between patients and caregivers, and how goals support pediatric patients' transition to self-care.
Will be published in CSCW 2021
I have also developed low-cost sensing to enable at-home monitoring for physiological function and sports training.
E-archery: Prototype Wearable for Analyzing Archery Release
A wearable archery training device system consisted of a glove and compatible Android app that detects archer’s hand motion, performs form classification, and gives feedback regarding the archer’s form.
Published in Ubicomp 2016
AirTech: Home-Use Lung Function Monitoring Device
Partnered with Micro-C, developed a lung function monitoring device for chronic lung disease patients that quantifies air flow rate and exhaled gas components, conducts test validity check, and automatically records test results to compatible iOS application.
I started my journey as a biomedical engineer focused on neural engineering, where I developed computer vision method to understand brain activity and neural activation.
Reverse-Correlation Analysis of Mechanosensation Circuit in C. elegans Reveals Temporal and Spatial Encoding
We use a custom tracking and optogenetics platform to characterize and compare two mechanosensory systems in C. elegans: the gentle touch sensing TRNs and harsh touch sensing PVDs. Through system modeling and computer vision techniques, we developed linear filters that capture dynamics that are consistent with previous findings, as well as provided new insights on the spatial encoding of the TRN and PVD neurons. Our results capture the overall dynamics of behavior induced by the activation of sensory neurons, providing simple transformations that fully characterize these systems.
Published in Nature Scientific Reports 2019
Investigating the Intersession Reliability of Dynamic Brain-State Properties
We analyzed resting-state functional magnetic resonance imaging data from 100 Human Connectome Project subjects were compared across 2 scan days. Brain states (i.e., patterns of coactivity across regions) were identified by classifying each time frame using k means clustering with and without global signal regression (GSR). We investigated the consistency in the brain-state properties across days and GSR attenuated the reliability of the brain states as well as changes in the brain-state properties across the course of the scan were investigated as well. The results demonstrate that summary metrics describing the clustering of individual time frames have adequate test/retest reliability, and thus, these patterns of brain activation may hold promise for individual-difference research.
Published in Brain Connectivity 2018