About the webinar:
This seminar highlights lessons learned from ambulatory studies for collecting continuous multi-modal human data and analysis & modeling for health, well-being and performance. The talk will provide an overview of the objectives of the studies, challenges faced in human daily life data collection and analysis using surveys, wearable sensors and mobile phones, and some key findings focused on detecting sleep patterns and detecting and forecasting mood changes using machine learning.
About the presenter:
Akane Sano is an Assistant Professor at Rice University Department of Electrical Computer Engineering and Computer Science. Her research focuses on human sensing, data analysis and application development for health and well-being. She is a member of Scalable Health Labs. Dr. Sano has been working on developing technologies to measure, forecast, understand and improve health and wellbeing. She has worked on measuring and predicting stress, mental health, sleep and performance and designing systems to help people to reduce their stress and improve their mental health, sleep and performance for student and employee populations. Some of Dr. Sano’s research projects include the SNAPSHOT study project, Eureka project (symptom prediction and digital phenotyping in schizophrenia using phone data) and mPerf project (Using mobile sensors to support productivity and employee well-being). She obtained her PhD at MIT Media Lab, and her MEng and BEng at Keio University, Japan. Before she joined Rice University, she was a Research Scientist in Affective Computing Group at MIT Media Lab, and a visiting scientist/lecturer at People-Aware Computing Lab, Cornell University. Before she came to the US, she was a researcher/engineer at Sony Corporation on wearable computing, intelligent systems and human computer interaction.