Emotion AI || Affective Computing - Development of NexGen

We are in the world where technology governs the way we move. How it feels like when a robot comes to you and wipe away your tears. You won't believe, but the future is always unexpected.

Affective computing is a new term coined for the study of emotion AI which comprises of development of those systems that has the capability to interpret, analyze, process, recognize and stimulate human emotions, facial expressions and effects. This field is multiple combinations of interdisciplinary subjects connecting applied computer science with cognitive science, artificial intelligence human, and psychology. Here the system developed for measuring emotions fetch data from facial expressions, and gestures, body language, and voice frequency and strength for measuring the emotions in human. IoT appliances and other home devices like AC (Air Conditioner), Smart Home devices like Alexa or Echo. Affective computing has the capability of humanizing the digital interactions (which are) with these specially developed systems.

  Various research topics and contents are being written on this trending field of AI. Affective computing machines have the capability in interpreting various human emotions and emotional states and adapt the behavior of these systems with humans (mostly done by virtual assistants or smart home assistant) which leads to giving a suitable response to those human generated emotions. Other business that is now experiencing this technology as their frontend concept for exploring customer and building relationships with users’ are CRM (Customer Relationship Management), HRM (Human Resource Management), sales and marketing, entertainment through VR and AR in combination with artificial intelligence for making games and interactive application more customer’s behaviour oriented etc.

  Affective computing uses various peripheral devices in order to detect and sense the state of emotion of any user through microphones, camera embedded with facial image recognition, sound and pitch measurement through software, timing of responses or human reflexes for triggering the digital input, as well as respond to different events in an interactive application such as changing the difficulty of a game or scenario of any level in a game or even changing the question patterns in quiz as well as genre of songs or videos chosen. All these factors and entities play a major role in the development of an understanding of a system for its user. With the increase in the digitalization of our lives, we are getting more inclined towards systems and gadgets that are polite in behavior and adjusting nature along with socially smart and active; which is the reason why users don’t want unnecessary information to be popped up and bothering the user to react on those. This concept of computing requires additional reasoning in combination with digital common sense as well as analysis of previously taken data.

  Various elements of data collecting mechanism for an affective computing are –

• ?  Gesture recognition
• ?  Posture analysis
• ?  Speech and voice quality and pitch
• ?  Force and pattern of keystrokes
• ?  Choices and tastes of digital contents
• ?  Sensors measuring hand and body temperature (with respect to the situation and mental condition of a user)
• ?  Movement of mouse or delivering of command to virtual assistants
• ?  Daily events and pattern of work

This field of computing not only detects and understands human or users’ emotions but also deals with emotions in machines. While human emotions get initiated through hormone secretion and other neuropeptides and events & environments, machines generate emotions through abstract states allied with evolution (or deficient in of development) in autonomous, supervised or unsupervised learning method, which is totally logical. Let suppose a machine is trained with some data to do a particular work by analyzing a user’s day to day behavior; but due to lack of efficient algorithm design, it might take a longer time to learn the users’ behavior. Also according to user’s emotional situation, it can correlate the fact that user is not satisfied with its understanding of learning and grabbing new things, which is negative feedback received as an additional input to the system which is processed under affective computation of machines.