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Affective computing is computing that deals with the attempt to make machines which can detect and respond to human emotion. ("Affect" is a synonym for "emotion" in the field of psychology.) In the simple case, this may be a computational device helping communicate the emotions of its user, either for personal reflection, or to increase the bandwidth of communication between people. The term "Affective Computing" was the title of an article by Professor Rosalind Picard at the MIT Media Lab in 1995. Affective computing is an interdisciplinary study including computer sciences, psychology, and cognitive science. Detecting and recognizing emotional information Detecting emotional information usually involve sensors which gather information about the user's physical state or behavior without interrupting the user. The most obvious way in which a computing device can sense the user's emotion is by using the same cues as other humans do, such as Facial expression, posture, gestures, and speech. Computing devices can also sense emotion in ways which humans are not capable of, such as the force or rhythm of key strokes of a hand on the keyboard, the temperature changes of a hand on the mouse, or the evaluation of other physiological vital signs. Other technologies such as speech recognition are being explored for gathering emotional information. Recognizing emotional information requires the extraction from the sensor data of the features specific to emotional states, and the learning of patterns of data by the software. Emotion in machines Another area within affective computing is the building of computational devices having (or simulating) emotions, whether this is an internal state of the software or something outwardly expressed in a way which can be seen by the user. For a machine to "have" emotion means there is a mechanism for it to decide which emotion state the machine should be in, and also influences the behavior of the system afterward. This may be useful in human-computer interactions with mechanisms such as robots and virtual reality systems. Expressing emotional information in such systems can enhance the naturalness of the system. Emotional understanding Emotional understanding refers to the ability of a device not only to detect emotional or affective information, but also to store, process, build and maintain an emotional model of the user. Emotional understanding aims at incorporating contextual information about the user and the environment, and produce appropriate responses. It is a difficult issue because human emotions arise from complex external contexts. Possible features of a system which displayed emotional understanding might be editable preferences such as avoidance or modification of interaction when the user is angry, and applications might improve security or confidentiality as well as the overall interaction. Emotional Speech Emotional Speech Processing recognize the user's emotional state by analysing speech patterns. Vocal parameters and prosody features such as pitch variables, speaking rate are analyzed through pattern recognition. Some related works: Dellaert, Lee The use of emotional inflection in machine-generated speech is very useful in human-computer interaction, especially on dialogue systems, making the speech natural and expressive. This includes both acoustic features, and also the content of the speech such as the words and phrases used. Facial Expression Facial Expression is another important technology to communicate emotions. Some methods used to analyze facial expression are optical flow and active appearance model. Hidden Markov Model and Neural network are some methods used to recognize the facial expression. Body gesture Body gesture is the position and the changes of the body. There are many proposed methods to detect the body gesture. Hand gestures had been a common focus of body gesture detection, apparentness methods and 3-D modeling methods are traditionally used. Potential Application In e-learning application, affective computing can be used to adjust the presentation of a computerized tutor when a learner is bored, interested, frustrated, or pleased. Psychological health services such as counseling, can be benefit by affective computing to collect client's emotional state. Robots with emotions can also work at a complex and uncertainty environment with a higher flexibility. Companion devices such as Digital pet may also make use of affective computing abilities to express the pet's emotion, model the emotion of pets for autonomous or even understand the pet owner's emotion. Affective computing had also been suggested to apply in monitoring society. For example a car which can monitor the emotion of driver may avoid car accident when the driver is angry. Affective computing have a high potential of application in human computer interaction, ideas like affective mirror which let the user see how he perform in front of others, emotion monitoring agent which warn you before you send negative or angry email, or even a music player which can create a map between music and emotion to select the music based on your mood had also been suggested. Application Examples | |||||||
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