Shiny blue brain

AI can detect human emotions with wireless signals

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An AI has been developed that can reportedly use wireless signals to reveal peoples’ inner emotions.

According to researchers from Queen Mary University of London, the use of radio waves to measure heartrate and breathing signals can be used to predict how someone is feeling even in the absence of any other visual cues, such as facial expressions.

Participants were initially asked to watch a video selected by researchers for its ability to evoke one of four basic emotion types: anger, sadness, joy and pleasure.

Whilst the individual was watching the video, the researchers sent radio signals like those transmitted by wireless systems, such as radar or Wi-Fi, towards the individual and measured the signals that bounced back off them. By analysing changes to these signals caused by slight body movements, the researchers were able to reveal ‘hidden’ information about an individual’s heart and breathing rates.

Previous research has used similar non-invasive or wireless methods of emotion detection. However, in these studies data analysis has depended on the use of classical machine learning approaches, whereby an algorithm is used to identify and classify emotional states within the data.

For this study, the scientists employed deep learning techniques, where an artificial neural network learns its own features from time-dependent raw data, and showed that this approach could detect emotions more accurately than traditional machine learning methods.

Achintha Avin Ihalage, a PhD student at Queen Mary, said: “Deep learning allows us to assess data in a similar way to how a human brain would work, looking at different layers of information and making connections between them.

“Most of the published literature that uses machine learning measures emotions in a subject-dependent way, recording a signal from a specific individual and using this to predict their emotion at a later stage.

“With deep learning, we’ve shown we can accurately measure emotions in a subject-independent way, where we can look at a whole collection of signals from different individuals and learn from this data and use it to predict the emotion of people outside of our training database.”

Methods to detect human emotions are often used by researchers involved in psychological or neuroscientific studies, but these approaches could also have wider implications for the management of health and wellbeing.

Ahsan Noor Khan, a PhD student at Queen Mary and first author of the study, said: “Being able to detect emotions using wireless systems is a topic of increasing interest for researchers as it offers an alternative to bulky sensors and could be directly applicable in future ‘smart’ home and building environments.

“In this study, we’ve built on existing work using radio waves to detect emotions and show that the use of deep learning techniques can improve the accuracy of our results.

“We’re now looking to investigate how we could use low-cost existing systems, such as Wi-Fi routers, to detect emotions of a large number of people gathered, for instance in an office or work environment.”

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