Rhythmic micro-gestures: discreet interaction on-the-go

Abstract

We present rhythmic micro-gestures, micro-movements of the hand that are repeated in time with a rhythm. We present a user study that investigated how well users can perform rhythmic microgestures and if they can use them eyes-free with non-visual feedback. We found that users could successfully use our interaction technique (97% success rate across all gestures) with short interaction times, rating them as low di culty as well. Simple audio cues that only convey the rhythm outperformed animations showing the hand movements, supporting rhythmic micro-gestures as an eyes-free input technique.

A Gesture Elicitation Study of Nose-Based Gestures

“…Although single areas represent a total amount of 85%, multiple areas were selected in 15% of cases. These results refine suitable areas [ 21 ].…”

“…Some nose gestures have never been elicited, because of their underlying connotation or social acceptance [ 21 ]. For example, the gesture where the thumb is put on the apex and the rest of the hand is extended in space to mean defiance, disrespect, or derision; the gesture is avoided for the same reason; the , which moves a nostril up and down or left to right, is physically uncomfortable to produce; the gesture because it could be painful, it communicates forgetfulness [ 50 ]; and gestures with a strong connotation: (a) putting the full hand on the nose, (b) rubbing the whole hand, (c) the “shut up” gesture, and (d) the gesture.…”

“…They found that some facial gestures are more perceived in public than others. Freeman et al [ 21 ] reports that most preferred regions are: cheek (34%), forehead (16%), jaw (8%), apex of the nose and others like chin, ear, and temple having more or less 7%. Although the cheek received the largest percentage (34%) due to its maximal surface, the alae and philtrum were not tested, thus leaving the potential of the nose full surface unexplored.…”

“…Therefore, male and female should in principle elicit the same gestures, except if these people experience social acceptance differently [ 20 ]. Whether the gender influences the gestures elicited remains untouched, knowing that human face is subject to social acceptation before preference [ 21 ]. Inspiration could also come from the field of communication, where body language expresses a wide range of knowledge about the use and interpretation of gestures [ 50 ].…”

Designing Motion Matching for Real-World Applications

“…Current work on motion matching interfaces has explored a variety of ways to capture user input, with the majority relying on optical tracking. Examples include systems that track users' eyes as these follow a moving target [18,25,27,48,52]; depth-cameras that track users' hands [9,21,22]; and systems that rely on off-the-shelf web-cams to capture any input motion in their field-of-view (FOV), be it performed by the users' hands, feet, or even their heads [11]. But due to inherent limitations of computer vision, such as being restricted by their FOV (interaction space), being susceptible to changing-light conditions and occlusion, and introducing privacy concerns when used in the context of smart homes [7], recent work looks at other forms of input sensing for motion matching.…”