µGeT: Multimodal eyes-free text selection technique combining touch interaction and microgestures

Abstract

We present µGeT, a novel multimodal eyes-free text selection technique. µGeT combines touch interaction with microgestures. µGeT is especially suited for People with Visual Impairments (PVI) by expanding the input bandwidth of touchscreen devices, thus shortening the interaction paths for routine tasks. To do so, µGeT extends touch interaction (left/right and up/down flicks) using two simple microgestures: thumb touching either the index or the middle finger. For text selection, the multimodal technique allows us to directly modify the positioning of the two selection handles and the granularity of text selection. Two user studies, one with 9 PVI and one with 8 blindfolded sighted people, compared µGeT with a baseline common technique (VoiceOver like on iPhone). Despite a large variability in performance, the two user studies showed that µGeT is globally faster and yields fewer errors than VoiceOver. A detailed analysis of the interaction trajectories highlights the different strategies adopted by the participants. Beyond text selection, this research shows the potential of combining touch interaction and microgestures for improving the accessibility of touchscreen devices for PVI.