Typealike

Abstract

We propose a style of hand postures to trigger commands on a laptop. The key idea is to perform hand-postures while keeping the hands on, beside, or below the keyboard, to align with natural laptop usage. 36 hand-posture variations are explored considering three resting locations, left or right hand, open or closed hand, and three wrist rotation angles. A 30-participant formative study measures posture preferences and generates a dataset of nearly 350K images under different lighting conditions and backgrounds. A deep learning recognizer achieves over 97% accuracy when classifying all 36 postures with 2 additional non-posture classes for typing and non-typing. A second experiment with 20 participants validates the recognizer under real-time usage and compares posture invocation time with keyboard shortcuts. Results find low error rates and fast formation time, indicating postures are close to current typing and pointing postures. Finally, practical use case demonstrations are presented, and further extensions discussed.