[Seminários 2023] Activity and Behavior Recognition for Healthcare and Wellness
Activity and Behavior Recognition for Healthcare and Wellness
Abstract
One of the main characteristics of ubiquitous computing systems is their reliance on contextual information to adapt the application to better assist the user. Advances in artificial intelligence are making it possible to infer user activities and behaviors to better tailor pervasive healthcare applications to user needs as well as to measure digital biomarkers and phenotypes. These activity and behavior-aware systems can be used in novel healthcare applications such as measuring the effects of medication in patients suffering from mental disorders, providing feedback in rehabilitation therapies, or perceiving abnormal behaviors that provide early signs of ailments such as Alzheimer’s disease. I will provide examples of research conducted at the Mobile and Ubiquitous Healthcare Laboratory at CICESE in this field.
Dr. Jesús Favela Vara
Jesús Favela is a Professor of Computer Science at, Mexico, where he leads the Mobile and Ubiquitous Healthcare Laboratory. His research interests include Ubiquitous Computing, Human-Computer Interaction, and Medical Informatics. Much of his research efforts have focused on the design and evaluation of ambient computing environments for healthcare, mostly to assist the demanding conditions of hospital work and to support older adults and their caregivers. Dr. Favela holds a BSc from the Universidad Nacional Autónoma de Mexico (UNAM) and an MSc and PhD from the Massachusetts Institute of Technology (MIT). He is a member of the Association for Computing Machinery (ACM), and former President of the Mexican Computer Science Society.