Solofinger: Robust microgestures while grasping everyday objects

Abstract

Using microgestures, prior work has successfully enabled gestural interactions while holding objects. Yet, these existing methods are prone to false activations caused by natural fnger movements while holding or manipulating the object. We address this issue with SoloFinger, a novel concept that allows design of microgestures that are robust against movements that naturally occur during primary activities. Using a data-driven approach, we establish that single-fnger movements are rare in everyday hand-object actions and infer a single-fnger input technique resilient to false activation. We demonstrate this concept's robustness using a white-box classifer on a pre-existing dataset comprising 36 everyday hand-object actions. Our fndings validate that simple SoloFinger gestures can relieve the need for complex fnger confgurations or delimiting gestures and that SoloFinger is applicable to diverse hand-object actions. Finally, we demonstrate SoloFinger's high performance on commodity hardware using random forest classifers.