Machine Learning and Sensing for Remote BiomedicinePresentation Transcript
Machine Learning and Sensing for Remote Biomedicine Tomás Lozano-Pérez MIT CSAIL
Remotely assisted living
Growth of elderly population a major challenge for 21 st century
Exploit cost-effective communication, computation and robotics to provide support for elderly
On-site robotic assistance
Remote medical monitoring
Pervasive and Transparent
Existing computer systems are inadequate for our goal
Computers must live in user’s world not force users to live in computer’s world
Reduce need for careful prior design
Adapt to wide range of environments
Understand the user
Adapting to users
People and Machines
Adapting to computational environment
Computers and Networks
Adapting to physical environment
Sensors and Effectors
Adapting to Users
Where are they?
Do they know Jane called?
Are they depressed?
Cultural and social milieu
Currency, food, family, etc.
Adapting to Physical Environment
Audio, video, haptics, X-ray/MRI, etc
Displays, speakers, robots, etc
Adapting to Computational Environment
Bandwidth and latency
Computing resources available
A Scenario: SARS management Hospital Contact Tracing Hospital Admission Treatment Recovery Disease Worsening Patient Discharged Calling Ambulance Community Infection Control Collecting Disease Information Supporting Clinical and Policy Research Public Education Courtesy: Leong Tze Yun