Reconfigurable natural interaction in smart environments: approach and prototype implementation

  • Authors:
  • Sara Bartolini;Bojan Milosevic;Alfredo D'Elia;Elisabetta Farella;Luca Benini;Tullio Salmon Cinotti

  • Affiliations:
  • ARCES--Università di Bologna, Bologna, Italy 40125;DEIS--Università di Bologna, Bologna, Italy 40136;ARCES--Università di Bologna, Bologna, Italy 40125;DEIS--Università di Bologna, Bologna, Italy 40136;DEIS--Università di Bologna, Bologna, Italy 40136;ARCES--Università di Bologna, Bologna, Italy 40125

  • Venue:
  • Personal and Ubiquitous Computing
  • Year:
  • 2012

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Abstract

The vision of sensor-driven applications that adapt to the environment hold great promise, but it is difficult to turn these applications into reality because device and space heterogeneity is an obstacle to interoperability and mutual understanding of the smart devices and spaces involved. Smart Spaces provide shared knowledge about physical domains and they inherently enable cooperative and adaptable applications by keeping track of the semantic relations between objects in the environment. In this paper, the interplay between sensor-driven objects and Smart Spaces is investigated and a device with a tangible interface demonstrates the potential of the $${\sl smart{\text -}space{\text -}based}$$ and $${\sl sensor{\text -}driven}$$ computing paradigm. The proposed device is named REGALS (Reconfigurable Gesture based Actuator and Low Range Smartifier). We show how, starting from an interaction model proposed by Niezen, REGALS can reconfigure itself to support different functions like Smart Space creation (also called $${\sl environment\,smartification}$$ ), interaction with heterogeneous devices and handling of semantic connections between gestures, actions, devices, and objects. This reconfiguration ability is based on the context received from the Smart Space. The paper also shows how tagged objects and natural gestures are recognized to improve the user experience reporting a use case and the performance evaluation of REGALS' gesture classifier.