Data on our emotions? The rise of affective computing

Machines are being developed that can read our emotions, with applications in healthcare, product research, and safety.
7 November 2019

Affective computing could cause some angst, but it must be developed with privacy policies foremost. Source: Shutterstock

Given the amount of data available to the world’s tech giants— whether with the individual’s implicit knowledge or not— companies have immense analytical power. 

Thanks to its users telling the platform more or less everything about themselves, Facebook can essentially predict its users’ future. But, regardless of how many personal data and browsing data we choke up to the digital realm, so far our real thoughts, feelings, and emotions are still private to ourselves— we hope at least. 

Enter emotional analytics, otherwise known as “affective computing”, but so far— thanks to the prevalence of data consent processes— use cases are more fascinating than sinister. 

Using biometric technology such as facial recognition and artificial intelligence (AI), affective computing seeks to understand and analyze human emotion to quantify how someone ‘really’ feels about a particular product or service. 

Insight from emotions

The idea is that the analysis of naturally, emotionally-charged reactions could provide more accurate user feedback in research and development than traditional means, such as surveys, interviews, and ratings. 

An ice cream maker, for example, could analyze how their target buyer reacts to their product.  

Other applications of the technology are in education; in e-learning, in particular, affective computing can be used to adjust the presentation of computerized tutors if the student is bored, interest, frustrated or pleased.

In healthcare, meanwhile, affective computing is being developed for use in ‘social robots’, which can help to relieve the burden on healthcare professionals, such as areas with aging populations, while replicating ‘bedside manner’ and applying the correct program based on the emotional responses of patients. 

This approach is also being extended to the development of communicative technologies for people with autism. Applications are vast, though; they could transform in-car safety; media players could select content based on user mood, or warn you if you’re about to fire off an email in anger.

Ultimately, affective computing could enhance and quantify our understanding of ‘the unsaid’— and tell us how, in an increasingly automated and AI-driven world, we can create machines that can interact and communicate with people at a base, empathetic level. 

Speaking to GlobalData Technology, Josipa Majic, CEO and Founder of emotional analytics firm Tacit, said: “We collect biometric data in order to know exactly how you feel with millisecond precision but without asking you any questions. 

“Then we crunch the data to get the three most important pieces of information.

“One is emotional classification. So essentially, what is the emotion that you’re feeling? The second is cognitive workload, meaning the level of mental effort you have to put in in order to comprehend what’s being shown to you. The third one is stress. So the level of discomfort that you feel when faced with that specific experience, or stimuli.”

Using those three data points, the company can predict what would be the perfect product feature combination, and how it can be adapted if targeting different demographics.  

Biometrics privacy 

In an age of data harvesting and misuse, Majic understands that the advent of this kind of technology will be unwelcome news to many, who believe the technology could be used for surveillance and profiling, especially as the AI technology improves. 

In order for users of the technology to achieve benefits across a range of use cases, it is therefore vital that the development of affective computing is carried out in line with airtight principles and respect to privacy. Unsurprisingly, consent and understanding is key.

“The most important thing from a data protection privacy perspective is for you to be aware, as a respondent, what is being collected,” said Majic; “When does the collection start? When does it finish? And what is the single purpose of what the data will be used for?

“From biometric data, we can often detect any health or mental health problems. And our outcomes are designed in a way so everything is anonymized and randomized, so we never share raw data and it can’t be mined. 

“I think educating the market and educating the wider media in what this is, how this will most likely be deployed, both by tech companies, public institutions, health companies and anything in between is important,” she concludes. 

Providing organizations the ability to read between the lines in regards to reactions to their products and services, providing powerful and rich new data sources, it’s no surprise this area of analytics is seeing rapid investment.

According to research by MarketsandMarkets, the affective computing market which is comprised of touch-based and touchless technology, speech and gesture recognition software, and purpose-designed sensors, cameras, and processors, will scale from US$22.2 billion in 2019 to US$90 billion by 2024. 

Major factors driving the market, according to the research include the growing demand for voice-enabled assistants, soaring need to counter fraudulent activities and enhanced security in the automotive and banking sectors.