Multi-finger chords for hand-held tablets

Abstract

Despite the demonstrated benefits of multi-finger input, todays gesture vocabularies offer a limited number of postures and gestures. Previous research designed several posture sets, but does not address the limited human capacity of retaining them. We present a multi-finger chord vocabulary, introduce a novel hand-centric approach to detect the identity of fingers on off-the-shelf hand-held tablets, and report on the detection accuracy. A between-subjects experiment comparing "random" to a "categorized" chord-command mapping found that users retained categorized mappings more accurately over one week than random ones. In response to the logical posture-language structure, people adapted to logical memorization strategies, such as 'exclusion', 'order', and 'category', to minimize the amount of information to retain. We conclude that structured chord-command mappings support learning, short-, and long-term retention of chord- command mappings.