Abstract

As we live longer, we are also living with more diseases. The need to identify illness symptoms and manage them has been increasing rapidly. Our personal devices have a role to play in helping us take care of ourselves outside a doctor’s clinic or hospital. Technology’s role in healthcare is already quite ubiquitous in the form of step counters and heart rate monitors. However, we can go far beyond these coarse measures. I will provide an overview of our efforts at building real-time machine-learning systems that measure depression symptoms, fatigue, sleep quality, and hyperactivity. While useful, these machine learning systems will probably never be perfectly accurate. The sensed information will always be noisy. Moreover, the user won’t know how to interpret and use measured information. I will present our ideas on how to make the inferred information actionable to the patients, caretakers, and doctors. I will also talk about the role of noisy machine learning systems and how a user can counter the system’s inherent error and uncertainty. Ultimately, we aim to build systems that help us manage our health without requiring perfectly accurate inferences.

About the speaker

Mayank is an Associate Professor in the Software and Societal Systems Department (S3D) and Human-Computer Interaction Institute (HCII) in the School of Computer Science at CMU. He leads the Smart Sensing for Humans (SMASH) Lab at CMU. His group develops new, practical, and deployable sensing and machine-learning solutions to solve problems in healthcare and reduce usage barriers for under-resourced populations. He regularly collaborates with mechanical and biomedical engineers, doctors, nurses, community health workers, and patients and their caregivers worldwide. His systems are currently used by hundreds of patients and deployed in several clinics and hospitals around the world.