Machine Learning and Sensing for Remote Biomedicine

Machine Learning and Sensing for Remote Biomedicine Tomás Lozano-Pérez MIT CSAIL

Remotely assisted living ,[object Object],[object Object],[object Object],[object Object]

Pervasive and Transparent ,[object Object],[object Object]

In general… ,[object Object],[object Object],[object Object]

Adaptive Computing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Adapting to Users ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Adapting to Physical Environment ,[object Object],[object Object],[object Object],[object Object]

Adapting to Computational Environment ,[object Object],[object Object],[object Object],[object Object]

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

Other applications ,[object Object],[object Object],[object Object],[object Object]

Fundamental Technological Needs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Getting there… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Machine Learning and Sensing for Remote Biomedicine

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