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Participatory design is a popular design technique that involves the endusers in the early stages of the design process to obtain user-friendlygestural interfaces. Guessability studies followed by agreement analyses areoften used to elicit and comprehend the preferences (or gestures/proposals) ofthe participants. Previous approaches to assess agreement, grouped the gesturesinto equivalence classes and ignored the integral properties that are sharedbetween them. In this work, we represent the gestures using binary descriptionvectors to allow them to be partially similar. In this context, we introduce anew metric referred to as soft agreement rate (SAR) to quantify the level ofconsensus between the participants. In addition, we performed computationalexperiments to study the behavior of our partial agreement formula andmathematically show that existing agreement metrics are a special case of ourapproach. Our methodology was evaluated through a gesture elicitation studyconducted with a group of neurosurgeons. Nevertheless, our formulation can beapplied to any other user-elicitation study. Results show that the level ofagreement obtained by SAR metric is 2.64 times higher than the existingmetrics. In addition to the mostly agreed gesture, SAR formulation alsoprovides the mostly agreed descriptors which can potentially help the designersto come up with a final gesture set.

Gesture Agreement Assessment Using Description Vectors