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STUDENT RESEARCH PRESENTATION: AFFECTIVE COMPUTING BY BUSYAIRI PRABU (ERIC) #308303458
[object Object],[object Object],[object Object],[object Object]
Affective Computing: Affective computing is computing that deals with the attempt to make machines which can detect and respond to human emotion. The term "Affective Computing" was the title of an article  by Professor Rosalind Picard at the MIT Media Lab in 1995  Reference: http://www.nationmaster.com/encyclopedia/Affective-computing
Academic Paper One: Affective Computing: Challenges Rosalind W. Picard MIT Media Laboratory, Room E15-020G, 20 Ames Street, Cambridge, MA 02139-4307, USA Received 17 January 2003; accepted 5 February 2003
Say Hello to SPOCK: Half-Vulcan/half-human Very intelligent Highly rational & Highly unemotional When someone never expresses  Emotion, it is tempting to think that  Emotion is not there.
Machines: Example:  Macintosh has been displaying  a smile for years when successful boot-up. People tend to confuse the smile as An emotional expression. Machines can fake the appearance Of an emotion, without having any Similar to those we have.
Rosalind W. Picard Explains: “ Machines that might actually “have”  Feelings is the key area of  Affective Computing.” In this paper: She addresses and responds To the criticisms and challenges which have Arisen in affective computing explaining Why it might be more valuable than Previously expected. Published books: Affective Computing, Picard 1997 & 2003
Biometric Surveillance: Face recognition using biometrics
Criticism: The range of mean and modalities of emotion expression is so broad, with many of These modalities being inaccessible (e.g., blood chemistry, brain activity,  Neurotransmitters), and many others being too non-differentiated. This makes it  Unlikely that collecting the necessary data will be possible or feasible in the  Near future.
Criticism 2: The sine qua non of emotion expression is  the physical body. To the extent that  computers do not have physical bodies,  they cannot reliably and believably express  emotion. Existing attempts at expressing emotions in robots which are at times  referred to as embodied, are unrealistic  and therefore unconvincing, and  unable to Generate the type of affective  responsiveness characterising  human-human interaction.
Coco – the interactive monitor: Ability to move its monitor, expressing ways that respond to and  encourage its user’s own postural movements.
Zizi the affectionate coach: Home of tomorrow Zizi growls when sat upon, purrs when touched and groans with delight  when you stroke her fur. If left alone, she mews for attention.
Academic Paper Two: Evaluating Affective Interactions: Alternatives to asking what users feel Rosalind W. Picard, Shaundra Bryant Daily MIT Media Laboratory, Room E15-020G, 20 Ames Street, Cambridge, MA 02139-4307, USA 2005 paper
Classical methods: Such as – Questionnaires: asking you what you feel now Interviews: with a video set up, recalling what happen earlier in the task These methods are valuable and are still used today, this paper will highlight Some alternatives to self-report of feelings.
The Paper: Divided into two categories: Body Measures  (e.g. Change in muscle activity) & Task measures (e.g. Better ability to a solve a creative Problem)
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Wearable Earring: Sense emotional status using blood volume pressure.
 
Task Measures of Affect: Findings show ways that affective states tend to influence various  behaviours during different tasks. Within different situations can change the outcome of an individuals Interaction changing their behaviours.
[object Object],[object Object],[object Object],[object Object],[object Object]
Academic Paper Three: Ambient Display using Musical Effects Luke Barrington, Michael J. Lyons, Dominique Diegmann, Shinji Abe University of California, San Diego 2006
The Paper Examine the communication of information about human affect, and construct A functioning prototype which captures behavioural activity level from the face  and maps it to musical affects.
 
 
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Bpra1751

  • 1. STUDENT RESEARCH PRESENTATION: AFFECTIVE COMPUTING BY BUSYAIRI PRABU (ERIC) #308303458
  • 2.
  • 3. Affective Computing: Affective computing is computing that deals with the attempt to make machines which can detect and respond to human emotion. The term "Affective Computing" was the title of an article by Professor Rosalind Picard at the MIT Media Lab in 1995 Reference: http://www.nationmaster.com/encyclopedia/Affective-computing
  • 4. Academic Paper One: Affective Computing: Challenges Rosalind W. Picard MIT Media Laboratory, Room E15-020G, 20 Ames Street, Cambridge, MA 02139-4307, USA Received 17 January 2003; accepted 5 February 2003
  • 5. Say Hello to SPOCK: Half-Vulcan/half-human Very intelligent Highly rational & Highly unemotional When someone never expresses Emotion, it is tempting to think that Emotion is not there.
  • 6. Machines: Example: Macintosh has been displaying a smile for years when successful boot-up. People tend to confuse the smile as An emotional expression. Machines can fake the appearance Of an emotion, without having any Similar to those we have.
  • 7. Rosalind W. Picard Explains: “ Machines that might actually “have” Feelings is the key area of Affective Computing.” In this paper: She addresses and responds To the criticisms and challenges which have Arisen in affective computing explaining Why it might be more valuable than Previously expected. Published books: Affective Computing, Picard 1997 & 2003
  • 8. Biometric Surveillance: Face recognition using biometrics
  • 9. Criticism: The range of mean and modalities of emotion expression is so broad, with many of These modalities being inaccessible (e.g., blood chemistry, brain activity, Neurotransmitters), and many others being too non-differentiated. This makes it Unlikely that collecting the necessary data will be possible or feasible in the Near future.
  • 10. Criticism 2: The sine qua non of emotion expression is the physical body. To the extent that computers do not have physical bodies, they cannot reliably and believably express emotion. Existing attempts at expressing emotions in robots which are at times referred to as embodied, are unrealistic and therefore unconvincing, and unable to Generate the type of affective responsiveness characterising human-human interaction.
  • 11. Coco – the interactive monitor: Ability to move its monitor, expressing ways that respond to and encourage its user’s own postural movements.
  • 12. Zizi the affectionate coach: Home of tomorrow Zizi growls when sat upon, purrs when touched and groans with delight when you stroke her fur. If left alone, she mews for attention.
  • 13. Academic Paper Two: Evaluating Affective Interactions: Alternatives to asking what users feel Rosalind W. Picard, Shaundra Bryant Daily MIT Media Laboratory, Room E15-020G, 20 Ames Street, Cambridge, MA 02139-4307, USA 2005 paper
  • 14. Classical methods: Such as – Questionnaires: asking you what you feel now Interviews: with a video set up, recalling what happen earlier in the task These methods are valuable and are still used today, this paper will highlight Some alternatives to self-report of feelings.
  • 15. The Paper: Divided into two categories: Body Measures (e.g. Change in muscle activity) & Task measures (e.g. Better ability to a solve a creative Problem)
  • 16.
  • 17. Wearable Earring: Sense emotional status using blood volume pressure.
  • 18.  
  • 19. Task Measures of Affect: Findings show ways that affective states tend to influence various behaviours during different tasks. Within different situations can change the outcome of an individuals Interaction changing their behaviours.
  • 20.
  • 21. Academic Paper Three: Ambient Display using Musical Effects Luke Barrington, Michael J. Lyons, Dominique Diegmann, Shinji Abe University of California, San Diego 2006
  • 22. The Paper Examine the communication of information about human affect, and construct A functioning prototype which captures behavioural activity level from the face and maps it to musical affects.
  • 23.  
  • 24.