Research!

Gaussian Process (GP) models have gained popularity for its flexibility to handle correlation among data sampled from distributions in the exponential family. The correlation frequently characterizes time-dependent data, such as step count data across different time horizons. In this project, we plan to analyze the effectiveness of Gaussian Process on zero-inflated Poisson (ZIP) step count data. To do so, we evaluate the performance of Gaussian Process in fitting a series of generative models approaching the ZIP distribution.

Parkinson’s disease (PD) is a neurodegenerative disease inducing dystrophy of the motor system. Automatic movement analysis systems have potential in improving patient care by enabling personalized and more accurate adjust of treatment. These systems utilize machine learning to classify the movement properties based on the features derived from the signals. Smartphones can provide an inexpensive measurement platform with their built-in sensors for movement assessment. This study compared three feature selection and nine classification methods for identifying PD patients from control subjects based on accelerometer and gyroscope signals measured with a smartphone during a 20-step walking test.

Mobile health (mHealth) aims to use smartphones and wearable sensors to deliver interventions to promote healthier behavior. Micro-randomized trial (MRT) is an experimental design to provide data to optimize mHealth interventions. In an MRT, each individual is repeatedly randomized among treatment options (e.g., receiving or not receiving a push notification) at hundreds or thousands of decision points. After each decision, a near-term, proximal outcome is measured. In this project, we focus on HeartSteps, a mHealth study where the intervention is a push notification for encouraging physical activity, and the proximal outcome is the minute-level step count of the individual for 30 minutes after the decision point.