Affective computing is the integration of emotional intelligence into machine devices so that they can recognise, interpret, process, and simulate human affects. It draws upon computer science, psychology, and cognitive science to enable ‘the human factor’ in artificial intelligence.

As artificial intelligence become more embedded into different machines, we will see more everyday applications over the next five to ten years.

Affective machines are able to understand the emotional state of a user. They rely on sensory input data and natural language processing to detect human emotions. This might include facial expressions, body temperature, heart rate etc. which can be used to indicate certain emotions.

As of 2015, some facial recognition machines are detecting emotions with 80 per cent accuracy. This capability will improve decision making – making it more realistic and reducing resilience to automated decision making.

It has vast applications, such as in retail, where facial recognition emotional analytics can be used to determine customers’ emotional reactions to products. At present, we only know whether they buy it or not – affective analytics will help us understand why. It can determine if they are satisfied or dissatisfied, looking lost and confused, or just plain bored.

It could also be used in psychology, or maybe even with stroke victims, where a patient is unable to express their feelings or thoughts. Affective computing can be used to understand what is going on inside the patient’s mind.