Abacus Gestures

Abstract

Designing an extensive set of mid-air gestures that are both easy to learn and perform quickly presents a significant challenge. Further complicating this challenge is achieving high-accuracy detection of such gestures using commonly available hardware, like a 2D commodity camera. Previous work often proposed smaller, application-specific gesture sets, requiring specialized hardware and struggling with adaptability across diverse environments. Addressing these limitations, this paper introduces Abacus Gestures, a comprehensive collection of 100 mid-air gestures. Drawing on the metaphor of Finger Abacus counting, gestures are formed from various combinations of open and closed fingers, each assigned different values. We developed an algorithm using an off-the-shelf computer vision library capable of detecting these gestures from a 2D commodity camera feed with an accuracy exceeding 98% for palms facing the camera and 95% for palms facing the body. We assessed the detection accuracy, ease of learning, and usability of these gestures in a user study involving 20 participants. The study found that participants could learn Abacus Gestures within five minutes after executing just 15 gestures and could recall them after a four-month interval. Additionally, most participants developed motor memory for these gestures after performing 100 gestures. Most of the gestures were easy to execute with the designated finger combinations, and the flexibility in executing the gestures using multiple finger combinations further enhanced the usability. Based on these findings, we created a taxonomy that categorizes Abacus Gestures into five groups based on motor memory development and three difficulty levels according to their ease of execution. Finally, we provided design guidelines and proposed potential use cases for Abacus Gestures in the realm of mid-air interaction.