Hackerman Hall B-17
Sleep, stress and mental health have been major health issues in modern society. Poor sleep habits and high stress, as well as reactions to stressors and sleep habits, can depend on many factors. Internal factors include personality types and physiological factors and external factors include behavioral, environmental and social factors. What if 24/7 rich data from mobile devices could identify which factors influence your bad sleep or stress problem and provide personalized early warnings to help you change behaviors, before sliding from a good to a bad health condition such as depression?
In my talk, I will present a series of studies and systems we have developed at MIT to investigate how to leverage multi-modal data from mobile/wearable devices to measure, understand and improve mental wellbeing.
First, I will talk about methodology and tools I developed for the SNAPSHOT study, which seeks to measure Sleep, Networks, Affect, Performance, Stress, and Health using Objective Techniques. To learn about behaviors and traits that impact health and wellbeing, we have measured over 200,000 hours of multi-sensor and smartphone use data as well as trait data such as personality from about 300 college students exposed to sleep deprivation and high stress.
Second, I will describe statistical analysis and machine learning models to characterize, model, and forecast mental wellbeing using the SNAPSHOT study data. I will discuss behavioral and physiological markers and models that may provide early detection of a changing mental health condition.
Third, I will introduce recent projects that might help people to reflect on and change their behaviors for improving their wellbeing.
I will conclude my talk by presenting my research vision and future directions in measuring, understanding and improving mental wellbeing.
Akane Sano is a Research Scientist at MIT Media Lab, Affective Computing Group. Her research focuses on mobile health and affective computing. She has been working on measuring and understanding stress, sleep, mood and performance from ambulatory human long-term data and designing intervention systems to help people be aware of their behaviors and improve their health conditions. She completed her PhD at the MIT Media Lab in 2015. Before she came to MIT, she worked for Sony Corporation as a researcher and software engineer on wearable computing, human computer interaction and personal health care. Recent awards include the Best Paper Award at the NIPS 2016 Workshop on Machine Learning for Health and the AAAI Spring Symposium Best Presentation Award.
Hackerman Hall B-17
This talk will introduce a kinematic and dynamic framework for creating a representative model of an individual. Building on results from geometric robotics, a method for formulating a geometric dynamic identification model is derived. This method is validated on a robotic arm, and tested on healthy and muscular dystrophy subjects to determine the utility as a clinical tool. In order to capture kinematics of the human body we used Visual observations, either motion capture or the Kinect camera. In order to obtain the dynamical parameters of the individual, we used force plate and force sensors for robot attached to human hand. The work in progress is to use Ultrasound scanner and Acoustic myography in order to estimate the muscle strength. Our current representative kinematic and dynamic model outperformed conventional height/mass scaled models. This allows us for rapid, quantitative measurements of an individual, with minimal retraining required for clinicians. These tools are then used to develop a prescriptive model for developing assistive devices. This framework is then used to develop a novel system for human assistance. A prototype device is developed and tested. The prototype is lightweight, uses minimal energy, and can provide an augmentation of 82% for providing hammer curl assistance.
Ruzena Bajcsy (LF’08) received the Master’s and Ph.D. degrees in electrical engineering from Slovak Technical University, Bratislava, Slovak Republic, in 1957 and 1967, respectively, and the Ph.D. in computer science from Stanford University, Stanford, CA, in 1972. She is a Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley, and Director Emeritus of the Center for Information Technology Research in the Interest of Science (CITRIS). Prior to joining Berkeley, she headed the Computer and Information Science and Engineering Directorate at the National Science Foundation. Dr. Bajcsy is a member of the National Academy of Engineering and the National Academy of Science Institute of Medicine as well as a Fellow of the Association for Computing Machinery (ACM) and the American Association for Artificial Intelligence.