‘Emotion AI’ in a broad sense is a term to describe where machines replicate the way humans think. Also known as affective computing and artificial emotional intelligence, emotion AI is a subset of artificial intelligence that enables machines and algorithms to recognize and interpret human emotions by tracking and analyzing facial expressions, body language, speech, or even emotion or sentiment conveyed in written text. As a rapidly evolving field of AI, the area operates at the intersection of behavioral science, cognitive computing, computer science, data science, machine learning, and signal processing to analyze subtle cues in human microexpressions, voice patterns, and gestures.
According to Markets and Markets, the global human emotion detection and recognition market is developing rapidly and will grow from USD 19.5 billion in 2020 to USD 37.1 billion by 2026, at a CAGR of 13.3%. Significant influencers for this market expansion include speech-based emotion detection systems and the adoption of AI, machine learning, deep learning, and IoT technologies.
Image source Markets and Markets
Why does the marketing industry need emotion AI?
It’s no secret that emotion has a significant influence on human behavior. As humans rely on emotions to make their purchasing decisions, it becomes imperative to tap into the emotional perspective of customer behavior by combining emotion recognition technology with AI. Marketers can identify a customer’s personality traits and analyze their emotional responses to a company’s products or services. In-store customer behavior analytics with emotion AI technology helps provide insight into how customers feel about purchasing. If customers have favorable emotions associated with a brand, they are more likely to be loyal consumers. Capturing customer emotional responses with the help of sensors and cameras help marketing teams draft the future course of action to improve customer experience and increase sales.
Emotion AI technology doesn’t work purely based on rational intelligence but learns from every interaction by sensing intentions, understanding the cognitive and emotive pathways of human communication, and differentiating between literal and non-literal statements, ultimately helping marketers to reach their target audiences effectively.
Applications of emotion AI
There is a broad range of emotion AI applications in fields like healthcare, marketing, advertising, gaming, and robotics.
- – AI technology helps provide emotional support (with the help of nurse bots) to patients, reminding them about their medications, monitoring their well-being attentively by “talking with them,” and providing an undivided focus on a patient.
- – Chatbots powered with emotion AI can imitate a therapist, help automate talk therapy, and help guide humans through basic mental-health issues.
- – Emotion AI can help assist doctors with diagnosis and prescribe appropriate medical treatments.
- – Using facial and voice recognition-powered AI solutions, companies can mitigate risks and detect fraudulent insurance claims in real-time.
- – Emotion AI can offer personalized banking services, credit risk assessment, on-spot fact verification, risk scoring, and setup biometric facial recognition at ATMs.
Public service and law enforcement
- – Surveillance cameras in public places can detect the general public’s emotions to understand their overall mood and predict any possible issues.
- – Emotion AI can analyze audio and video recordings and perform real-time analysis of criminal suspects during interrogations.
- – Human-like robots engage in customer service, encourage inclusive education with customized teaching activities, and much more. Some humanoid robots examples include Sophia, T-HR3, and Milo robots4autism.
Types of emotion AI
With technological advancements and continuous R&D in the AI field, new prototypes and applications to address different aspects of emotion AI are coming into existence. Some of the most common types are:
Vision-based emotion AI systems – The advent of deep learning has tremendously boosted the development of vision-based systems where facial expressions and gestures are analyzed to determine the emotional state of humans.
Audio-based emotion AI systems – Acoustic, prosodic, spectral/frequency-based features like loudness, pitch, tempo, rhythm, and MFCC are analyzed to understand the emotional state of the human.
Multimodal emotion AI systems – Systems that combine audio, video, and text data to get a holistic understanding of the human’s emotional state and make human-machine communication more natural.
The field of emotion AI is evolving at a rapid pace with contextual awareness at the center of the study of emotions. Merging data collected from different modalities (like facial expressions, vocal signs, etc.) to assemble a single representation to understand an entire emotional state is challenging but is the natural course of AI and the future of this technology. However, as this technology continues to evolve, expect emotion AI to elevate systems to a whole new human-like level of intelligence.
To learn more about how emotion AI can transform your applications and systems to reach new levels of intelligence, send us your query to firstname.lastname@example.org. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with . develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellect2.ai.