Machine Learning and Sensing for Remote Biomedicine Tomás Lozano-Pérez MIT CSAIL
Remotely assisted living <ul><li>Growth of elderly population a major challenge for 21 st  century </li></ul><ul><li>Explo...
Pervasive and Transparent <ul><li>Existing computer systems are inadequate for our goal </li></ul><ul><li>Computers must l...
In general… <ul><li>Reduce need for careful prior design </li></ul><ul><li>Adapt to wide range of environments </li></ul><...
Adaptive Computing <ul><li>Adapting to users </li></ul><ul><ul><li>People and Machines </li></ul></ul><ul><li>Adapting to ...
Adapting to Users <ul><li>Physical environment </li></ul><ul><ul><li>Where are they? </li></ul></ul><ul><li>Goals </li></u...
Adapting to Physical Environment <ul><li>Sensors  </li></ul><ul><ul><li>Audio, video, haptics, X-ray/MRI, etc </li></ul></...
Adapting to Computational Environment <ul><li>Bandwidth and latency </li></ul><ul><li>Sensor/effector variation </li></ul>...
A Scenario: SARS management Hospital Contact Tracing Hospital Admission Treatment Recovery Disease Worsening Patient Disch...
Other applications <ul><li>Remote consultation </li></ul><ul><li>Tele-surgery </li></ul><ul><li>Distance education </li></...
Fundamental Technological Needs <ul><li>Signal to Symbol </li></ul><ul><ul><li>Object/Activity recognition </li></ul></ul>...
Getting there… <ul><li>Perception </li></ul><ul><ul><li>Vision, speech, medical imaging </li></ul></ul><ul><li>Machine Lea...
 
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Machine Learning and Sensing for Remote Biomedicine

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Machine Learning and Sensing for Remote Biomedicine

  1. 1. Machine Learning and Sensing for Remote Biomedicine Tomás Lozano-Pérez MIT CSAIL
  2. 2. Remotely assisted living <ul><li>Growth of elderly population a major challenge for 21 st century </li></ul><ul><li>Exploit cost-effective communication, computation and robotics to provide support for elderly </li></ul><ul><ul><li>On-site robotic assistance </li></ul></ul><ul><ul><li>Remote medical monitoring </li></ul></ul>
  3. 3. Pervasive and Transparent <ul><li>Existing computer systems are inadequate for our goal </li></ul><ul><li>Computers must live in user’s world not force users to live in computer’s world </li></ul>
  4. 4. In general… <ul><li>Reduce need for careful prior design </li></ul><ul><li>Adapt to wide range of environments </li></ul><ul><li>Understand the user </li></ul>
  5. 5. Adaptive Computing <ul><li>Adapting to users </li></ul><ul><ul><li>People and Machines </li></ul></ul><ul><li>Adapting to computational environment </li></ul><ul><ul><li>Computers and Networks </li></ul></ul><ul><li>Adapting to physical environment </li></ul><ul><ul><li>Sensors and Effectors </li></ul></ul>
  6. 6. Adapting to Users <ul><li>Physical environment </li></ul><ul><ul><li>Where are they? </li></ul></ul><ul><li>Goals </li></ul><ul><ul><li>Find glasses </li></ul></ul><ul><li>Activities </li></ul><ul><ul><li>Cooking dinner </li></ul></ul><ul><li>Knowledge state </li></ul><ul><ul><li>Do they know Jane called? </li></ul></ul><ul><li>Mental state </li></ul><ul><ul><li>Are they depressed? </li></ul></ul><ul><li>Cultural and social milieu </li></ul><ul><ul><li>Currency, food, family, etc. </li></ul></ul>
  7. 7. Adapting to Physical Environment <ul><li>Sensors </li></ul><ul><ul><li>Audio, video, haptics, X-ray/MRI, etc </li></ul></ul><ul><li>Effectors </li></ul><ul><ul><li>Displays, speakers, robots, etc </li></ul></ul>
  8. 8. Adapting to Computational Environment <ul><li>Bandwidth and latency </li></ul><ul><li>Sensor/effector variation </li></ul><ul><li>Computing resources available </li></ul><ul><li>New software/hardware </li></ul>
  9. 9. 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
  10. 10. Other applications <ul><li>Remote consultation </li></ul><ul><li>Tele-surgery </li></ul><ul><li>Distance education </li></ul><ul><li>Meeting support </li></ul>
  11. 11. Fundamental Technological Needs <ul><li>Signal to Symbol </li></ul><ul><ul><li>Object/Activity recognition </li></ul></ul><ul><li>Recognizing patterns </li></ul><ul><ul><li>Recognize deviations from normal behavior </li></ul></ul><ul><li>Goal oriented systems </li></ul><ul><ul><li>Achieve goals not follow rote instructions </li></ul></ul>
  12. 12. Getting there… <ul><li>Perception </li></ul><ul><ul><li>Vision, speech, medical imaging </li></ul></ul><ul><li>Machine Learning </li></ul><ul><ul><li>Fitting complex models, classification </li></ul></ul><ul><ul><li>Optimal data acquisition and decision making </li></ul></ul><ul><li>Modular, adaptive, distributed systems </li></ul><ul><ul><li>Adaptive software design methodology </li></ul></ul><ul><ul><li>Reconfigurable hardware </li></ul></ul><ul><li>Algorithms </li></ul><ul><ul><li>Real time algorithms </li></ul></ul><ul><ul><li>Correctness </li></ul></ul>
  13. 14. Operating Room Setup

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