No abstract

No abstract

Feet are the foundation of our bodies that not only perform locomotion but also participate in intent and emotion expression. Thus, foot gestures are an intuitive and natural form of expression for interpersonal interaction. Recent studies have mostly introduced smart shoes as personal gadgets, while foot gestures used in multi-person foot interactions in social scenarios remain largely unexplored. We present Shoes++, which includes an inertial measurement unit (IMU)-mounted sole and an input vocabulary of social foot-to-foot gestures to support foot-based interaction. The gesture vocabulary is derived and condensed by a set of gestures elicited from a participatory design session with 12 users. We implement a machine learning model in Shoes++ which can recognize two-person and three-person social foot-to-foot gestures with 94.3% and 96.6% accuracies (N=18). In addition, the sole is designed to easily attach to and detach from various daily shoes to support comfortable social foot interaction without taking off the shoes. Based on users' qualitative feedback, we also found that Shoes++ can support team collaboration and enhance emotion expression, thus making social interactions or interpersonal dynamics more engaging in an expanded design space.