is the study and development of systems and devices that can recognize,  interpret, process, and simulate human affects. Affective computing It is an interdisciplinary field spanning computer science, psychology, and cognitive science. While the origins of the field may be traced as far back as to early philosophical inquiries into emotion (“affect” is, basically, a synonym for “emotion.”), the more modern branch of computer science originated with 1995 paper on affective computing. A motivation for the research is the ability to simulate . The machine should interpret the emotional state of humans and adapt its behavior to them, giving an appropriate response for those emotions. Rosalind Picard’s empathy Affective  computing technologies sense the emotional state of a user (via sensors, microphone, cameras and/or software logic) and respond by performing specific, predefined product/service features, such as changing a quiz or recommending a set of videos to fit the mood of the learner. The  more computers we have in our lives the more we’re going to want them to behave politely, and be socially smart. We don’t want it to bother us  with unimportant information. That kind of common-sense reasoning requires an understanding of the person’s . emotional state One way to look at affective computing is in which a device has the ability to detect and appropriately respond to its user’s emotions and other stimuli. A computing device with this capacity could gather cues to user emotion from a variety of sources. Facial expressions, posture, gestures, speech, the force or rhythm of key strokes and the temperature changes of the hand on a mouse can all signify changes in the user’s emotional state, and these can all be detected and interpreted by a computer. A built-in camera captures images of the user and algorithm s are used to process the data to yield meaningful information. Speech recognition and gesture recognition are among the other technologies being explored for affective computing applications. human-computer interaction Recognizing emotional information requires the extraction of meaningful patterns from the gathered data. This is done using techniques that process different modalities, such as speech recognition, natural language processing, or facial expression detection. machine learning Emotion in machines A major area in affective computing is the design of computational devices proposed to exhibit either innate emotional capabilities or that are capable of convincingly simulating emotions. A more practical approach, based on current technological capabilities, is the simulation of emotions in conversational agents in order to enrich and facilitate interactivity between human and machine. While human emotions are often associated with surges in hormones and other neuropeptides, emotions in machines might be associated with abstract states associated with progress (or lack of progress) in autonomous learning systemsIn this view, affective emotional states correspond to time-derivatives in the learning curve of an arbitrary learning system. Two major categories describing emotions in machines: and . Emotional speech Facial affect detection Emotional speech includes: Algorithms Databases Speech Descriptors Facial affect detection includes: Body gesture Physiological monitoring The Future Affective computing tries to address one of the major drawbacks of versus in-classroom learning _ the teacher’s capability to immediately adapt the pedagogical situation to the emotional state of the student in  the classroom. In applications, affective computing can be used to adjust the presentation style of a computerized tutor when a learner is bored, interested, frustrated, or pleased. , i.e. counseling, benefit from affective computing applications when determining a client’s emotional state. online learning e-learning Psychological health services capable of processing affective information exhibit higher flexibility while one works in uncertain or complex environments. Companion devices, such as digital pets, use affective computing abilities to enhance realism and provide a higher degree of autonomy. Robotic systems Other potential applications are centered around . For example, a car can monitor the emotion of all occupants and engage in additional safety measures, such as alerting other vehicles if it detects the driver to be angry. Affective computing has potential applications in human computer interaction, such as affective mirrors allowing the user to see how he or she performs; emotion monitoring agents sending a warning before one sends an angry email; or even music players selecting tracks based on mood. Companies would then be able to use affective computing to infer whether their products will or will not be well received by the respective market. Social Monitoring There are endless applications for affective computing in all aspects of life. , Author the Books : Ahmed Banafa Secure and Smart Internet of Things (IoT) Using Blockchain and AI Blockchain Technology and Applications Read more articles at Technology Trends by Prof. Ahmed Banafa References https://en.wikipedia.org/wiki/Affective_computing http://www.gartner.com/it-glossary/affective-computing http://whatis.techtarget.com/definition/affective-computing http://curiosity.discovery.com/question/what-is-affective-computing

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