Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography We investigate how pressuresensitive smart textiles, in the form of a headband, can detect changes in facial expressions that are indicative of emotions and cognitive activities. Specifically, we present the Expressure system that performs surface pressure mechanomyography on the forehead using an array of textile pressure sensors that is not dependent on specific placement or attachment to the skin. Our approach is evaluated in systematic psychological experiments. First, through a mimicking expression experiment with 20 participants, we demonstrate the systemxe2x80x99s ability to detect welldefined facial expressions. We achieved accuracies of 0.824 to classify among three eyebrow movements (0.333 chancelevel) and 0.381 among seven fullface expressions (0.143 chancelevel). A second experiment was conducted with 20 participants to induce cognitive loads with Nback tasks. Statistical analysis has shown significant correlations between the Expressure features on a fine time granularity and the cognitive activity. The results have also shown significant correlations between the Expressure features and the Nback score. From the 10 most facially expressive participants, our approach can predict whether the Nback score is above or below the average with 0.767 accuracy.

Superpower Glass: Delivering Unobtrusive Realtime Social Cues in Wearable Systems We have developed a system for automatic facial expression recognition, which runs on Google Glass and delivers realtime social cues to the wearer. We evaluate the system as a behavioral aid for children with Autism Spectrum Disorder (ASD), who can greatly benefit from realtime noninvasive emotional cues and are more sensitive to sensory input than neurotypically developing children. In addition, we present a mobile application that enables users of the wearable aid to review their videos along with autocurated emotional information on the video playback bar. This integrates our learning aid into the context of behavioral therapy. Expanding on our previous work describing inlab trials, this paper presents our system and applicationlevel design decisions in depth as well as the interface learnings gathered during the use of the system by multiple children with ASD in an athome iterative trial.