Flagg, A., "Sensing and recognizing affective touch in a furry zoomorphic object,", M.Sc. Thesis, University of British Columbia, 2012.
Over the last decade, the surprising fact has emerged that machines can possess therapeutic power. Due to the many healing qualities of touch, one route to such power is in haptic emotional interaction, which in turn requires sophisticated touch sensing and interpretation. We explore the development of affective touch gesture recognition technologies in the context of a furry artificial lap-pet, with the ultimate goal of creating therapeutic interactions by sensing human emotion through touch. We design, construct, and evaluate a low-cost, low-tech furry lap creature prototype equipped with 2 types of touch-sensing hardware. The first of these hardware types is our own design for a new type of touch sensor built with conductive fur, invented and developed as part of this research. The second is an existing design for a piezoresistive fabric pressure sensor, adapted to three dimensions for our robot-body context. Combining features extracted from the time-series data output of these sensors, we perform machine learning analysis to recognize touch gestures. In a study of 16 participants and 9 key affective gestures, our model averages 94% gesture recognition accuracy when trained on individuals, and 86% accuracy when applied generally across the entire set of participants. The model can also recognize who out of the 16 participants is touching the prototype with an accuracy of 79%. These results promise a new generation of emotionally intelligent machines, enabled by affective touch gesture recognition.