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.

Supporting collaborative goal-setting for hospitalized adolescent patients

Collaborative goal-setting has been shown to be an effective way to encourage patient engagement and facilitate patient-provider communication. However, few studies have explored how hospitalized patients understand and use collaborative goal-setting to communicate with their care teams. Even less is known for how adolescent patients perceive collaborative goal-setting. We present a technology probe study aiming to explore how adolescent patients perceive collaborative goal-setting during hospitalization. We discussed the design process of the probe application, and identified the values and perspective that adolescent patients have on goal-setting.

[PDF] [App Github]

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.


Conceptualization of Personal Values for Patient-Provider Communication for patients with Multiple Chronic Conditions

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


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.

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.


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.