Emotion Learning: Unfold New Opportunities for Consumer


The theory of Emotion Learning services or ‘emotion AI’ enchants up perceptions of humanoid robots in roles of customer service.

According to Annette Zimmermann, research vice president at Gartner, “humanoid robotics is just one of many possible uses for emotion AI technology.”

“Gartner predicts that 10% of personal devices will have emotion AI skills by 2022.”

For a decade, Tech giants and smaller startups have been spending on Emotion AI by either voice analysis or computer vision to recognize human emotions. Many of these companies have already started working on research, analyzing, and capturing human emotions in response to a product or TV commercial. At the same time, commercial deployments are gradually developing in robotics, smart devices, cars, call centers, and virtual personal assistants (VPAs).

New uses are developing fast.

For the past two years, emotion AI sellers have advised into entirely new areas and sectors, assisting organizations in creating a better customer experience and unfastening real cost savings. These uses include:

Video gaming. Handling computer vision, the game console/video game identifies emotions via facial appearances and adjusts them.

Education. Education software prototypes have been produced to conform to kids’ emotions. When the child shows disappointment because a task is too challenging or too easy, the program adjusts the task to make it less or more difficult. Another learning system assists autistic kids in seeing other people’s emotions.

Medical diagnosis: Using voice analysis, the software can assist doctors in diagnosing diseases such as depression and dementia.

Patient care. A ‘nurse bot’ alerts older patients on long-term therapeutic schedules to take their medicine and talks with them every day to watch their complete well-being.

Employee safety: According to Gartner client inquiries, there increasing need for employee safety solutions 

 Emotion AI can assist in analyzing the stress and anxiety levels of workers who have very difficult jobs, such as primary responders.

Car safety: Computer vision technology is used by automotive sellers to observe the driver’s emotional status. A severe emotional state or drowsiness could trigger a signal for the driver.

Fraud detection: Voice analysis is used by insurance companies to detect whether a customer is telling the truth when submitting a claim. As per free surveys, up to 30% of users have adopted misleading information to their car insurance company to get coverage.

Autonomous car. It is predicted that the interior of autonomous cars will have several sensors, including cameras and microphones, to watch what is emerging and to know how users perspective the driving experience.

Call center intelligent routing: An annoyed client can be identified from the start and can be routed to a well-trained assistant who can also watch in real-time how the discussion is working and adapt.

Recruiting: During job interviews, the software is used to know the trustworthiness of a candidate.

Connected home. A VPA-enabled speaker can identify the mood of the person communicating with it and respond respectively.

Public service. Connections between emotion AI technology sellers, and surveillance camera providers have developed. In the United Arab Emirates, cameras in public places can identify people’s facial expressions and determine their general attitude. The country’s Ministry of Happiness started this project.

Retail. Retailers have begun to consider incorporating computer vision emotion AI technology into their businesses to record demographic data and visitor moods and emotions.

Final Lines

However, there are still obstacles to adoption. According to a recent Gartner consumer survey, there are still many trust difficulties surrounding Emotion Learning services; in particular, customers are less comfortable with emotion AI via video capture than emotion AI via voice analysis.

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