New study uses wireless signals for emotion detection

Published in the journal PLOS ONE, the study demonstrates the use of radio waves to measure heart rate and breathing signals to predict how someone is feeling in the absence of other visual cues such as facial expressions. 

PhD student at QMUL, Achintha Avin Ihalage, said their proposed deep learning approach is a novel neural architecture that can process time-dependent wireless signal and frequency-domain wavelet transformation images simultaneously while preserving temporal and spatial relationships. 

While the ‘basic building blocks’ used to implement the neural network are well known and widely adopted, added first author and PhD student Ahsan Noor Khan, the method of evoking emotions and its combination with wireless signals for unobtrusive sensing of breathing and heart rate is potentially new and could open doors for new opportunities in wireless emotion detection.

“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,” said Noor Khan. “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."