Augmented Reality can induce illusory perceptions of heat when users experience virtual flames rendered on their hands. In this exploratory study, we used high-density electroencephalography to investigate the neural correlates associated with this experience. We recorded neural activity in a within-subjects design during three key audiovisual conditions: (A) lifelike virtual flames on the hand paired with a burning sound; (B) a non-fire-like control stimulus (green, downward-flowing visuals with a synthetic sound) on the hand; and (C) realistic flames presented between the hands. Our preliminary analyses revealed distinct electroencephalography signatures between the Fire-on-Hand condition and both control conditions. Our findings suggest that AR-induced thermal illusions may engage brain networks involved in somatosensation, multisensory body representation, and spatial attention. This top-down, predictive mechanism appears to recruit neural systems that overlap with those involved in the higher-order cortical processing of actual thermal stimuli.

Abstract Abnormal behaviour can be an indicator for a medical condition in older adults. Our novel unsupervised statistical concept drift detection approach uses variational autoencoders for estimating the parameters for a statistical hypothesis test for abnormal days. As feature, the Kullback–Leibler divergence of activity probability maps derived from power and motion sensors were used. We showed the general feasibility (min. F 1 -Score of 91 %) on an artificial dataset of four concept drift types. Then we applied our new method to our real–world dataset collected from the homes of 20 (pre–)frail older adults (avg. age 84.75 y). Our method was able to find abnormal days when a participant suffered from severe medical condition.