Smart Arse: Posture Classification with Textile Sensors in Trousers

Abstract

Body posture is a good indicator of, amongst other things, people's state of arousal, focus of attention and level of interest in a conversation. Posture is conventionally measured by observation and hand coding of videos or, more recently, through automated computer vision and motion capture techniques. Here we introduce a novel alternative approach exploiting a new modality: posture classification using bespoke 'smart' trousers with integrated textile pressure sensors. Changes in posture translate to changes in pressure patterns across the surface of our clothing. We describe the construction of the textile pressure sensor and, using simple machine learning techniques on data gathered from 10 participants, demonstrate its ability to discriminate between 19 different basic posture types with high accuracy. This technology has the potential to support anonymous, unintrusive sensing of interest, attention and engagement in a wide variety of settings.