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Conference Papers Year : 2021

SoloFinger: Robust Microgestures while Grasping Everyday Objects


Using microgestures, prior work has successfully enabled gestural interactions while holding objects. Yet, these existing methods are prone to false activations caused by natural finger 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 pri- mary activities. Using a data-driven approach, we establish that single-finger movements are rare in everyday hand-object actions and infer a single-finger input technique resilient to false activation. We demonstrate this concept’s robustness using a white-box classifier on a pre-existing dataset comprising 36 everyday hand-object actions. Our findings validate that simple SoloFinger gestures can relieve the need for complex finger configurations 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 classifiers.
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Dates and versions

hal-03515523 , version 1 (06-01-2022)



Adwait Sharma, Michael A Hedderich, Divyanshu Bhardwaj, Bruno Fruchard, Jess Mcintosh, et al.. SoloFinger: Robust Microgestures while Grasping Everyday Objects. CHI '21: CHI Conference on Human Factors in Computing Systems, May 2021, Yokohama Japan, Japan. pp.1-15, ⟨10.1145/3411764.3445197⟩. ⟨hal-03515523⟩
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