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Did you know how affective computing can give machines "empathy"?
Affective computing is the discipline that studies and develops systems and devices that can recognize, interpret, process and simulate human emotions. It is an interdisciplinary field that spans computer science, psychology, and cognitive science. Early discussions of emotion can be traced back to human philosophical thinking about emotion, but the modern branch of computer science began with Rosalind Pickard's paper "Affective Computing" in 1995 and her book of the same name published in 1997. A major motivation in this field is to give machines emotional intelligence, including simulated empathy, so that machines can understand human emotional states and adjust their behavior based on these emotions, so that they can give appropriate emotional responses.
The goal of affective computing is to improve the interaction between people and machines and make it more humane.
Detecting and identifying emotional information
Detecting emotional information often starts with passive sensors that capture the user's physical state or behavior without interpreting the input data. This data is similar to the cues humans use to sense the emotions of others. For example, video cameras capture facial expressions, body postures and gestures, while microphones record speech. Other sensors detect emotional cues by directly measuring physiological data, such as skin temperature and electrocutaneous response.
Emotion in the machine
Another important area lies in the design of computing devices that can display intrinsic emotions or that can convincingly simulate emotions. Today's technologies are capable of simulating emotions, especially in conversational agents, which enhance and facilitate human-machine interactions. In The Emotional Machine, computer science pioneer Marvin Minsky connected emotions to broader questions of machine intelligence, noting that emotions are "not particularly alien to the process we call 'thinking.'" Digital Humans humans ) is an attempt to give these programs an emotional dimension, including reactions to certain emotional stimuli, and corresponding facial expressions and gestures.
Emotion in machines often involves the emotions of computing systems, all of which has given rise to the terms "emotional AI" and "emotional AI."
Technology in emotion recognition
In psychology, cognitive science, and neuroscience, there are two main ways to describe how humans perceive and classify emotions: the continuous approach and the categorical approach. In the emotion recognition process, the use of various machine learning regression and classification model technologies is also critical.
Emotional Voice
Changes in the autonomic nervous system can indirectly alter a person's speech, and emotion technology can use this information to identify emotions. For example, speech in states of fear, anger, or joy tends to be fast, loud, and clear, with a higher and wider pitch range, whereas tiredness, boredom, or sadness often results in slow, low-pitched, and slurred speech. Emotional speech processing technology can identify the user's emotional state through computational analysis of speech features.
Algorithm
The process of speech/text emotion detection requires the creation of a reliable database and knowledge base, and the selection of an effective classifier to achieve fast and accurate emotion recognition. With the development of technology, different algorithms have been proposed one after another. Using appropriate classifiers can significantly improve the overall performance of the system.
Choosing an appropriate classifier can significantly improve the accuracy and efficiency of emotion recognition.
Facial emotion recognition
The detection and processing of facial expressions are usually implemented through methods such as optical flow, hidden Markov models, and neural networks. In addition to the facial expression itself, multi-modal recognition can be performed by combining voice melody, gestures and other information to more accurately assess the subject's emotional state. Building an emotion database is a task that requires time and effort, and most public emotion databases only include gesture expressions, which makes facial emotion recognition challenging.
Body gestures
Gestures can be effectively used as a way to identify a user's specific emotional state, especially when combined with speech and facial expression recognition. Gesture recognition methods are mainly divided into two approaches based on 3D models and appearance. The computer should be able to understand these actions and make appropriate responses to promote the efficiency of human-computer interaction.
Physiological monitoring
This can detect a user's emotional state by monitoring and analyzing their physiological signals. Physiological signals include heart rate changes, skin conduction responses, contraction of small facial muscles, changes in blood flow, etc. This area has received further attention recently, and we are now seeing some real products already adopting these technologies.
As technology develops, affective computing plays an increasingly important role in daily life. Will machines of the future be capable of true empathy?
