SHOW

Abstract

Smart watch is becoming a new gateway through which people stay connected and track everyday activities, and text-entry on it is becoming a frequent need. With the two de facto solutions: tap-on-screen and voice input, text-entry on the watch remains a tedious task because 1. Tap-on-screen is error prone due to the small screen; 2. Voice input is strongly constrained by the surroundings and suffers from privacy leak. In this paper, we propose SHOW, which enables the user to input as they handwrite on horizontal surfaces, and the only requirement is to use the elbow as the support point. SHOW captures the gyroscope and accelerometer traces and deduces the user's handwriting thereafter. SHOW differs from previous work of gesture recognition in that: 1. it employs a novel rotation injection technique to substantially reduce the effort of data collection; 2. it does not require whole-arm posture, hence is better suited to space-limited places (e.g. vehicles). Our experiments show that SHOW can effectively generate 60 traces from one real handwriting trace and achieve high accuracy at 99.9% when recognizing the 62 different characters written by 10 volunteers. Furthermore, having more screen space after removing the virtual keyboard, SHOW can display 4x candidate words for autocompletion. Aided by the tolerance of character ambiguity and accurate character recognition, SHOW achieves over 70% lower mis-recognition-rate, 43% lower no-response-rate in both daily and general purposed text-entry scenarios, and 33.3% higher word suggestion coverage than the tap-on-screen method using a virtual QWERTY keyboard.