eWatch: Context Sensitive System Design Case Study

Smailagic, Asim; Siewiorek, D.P.; Maurer, U.; Rowe, Anthony; Tang, Karen P.

doi:10.1109/isvlsi.2005.31

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…There have been several research groups working on easily accessible displays at IBM, Hewlett-Packard, Microsoft Research, and CMU [11,14,6,7,16,15]. In addition, there have also been several commercial offerings with a wide range of applications and functions.…”

Section: Related Workmentioning

confidence: 99%

Context to Make You More Aware

Andrew

Anokwa

Koscher

et al. 2007

27th International Conference on Distributed Computing Systems Workshops (ICDCSW'07)

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Section: Related Workmentioning

confidence: 99%

Context to Make You More Aware

Andrew

Anokwa

Koscher

et al. 2007

27th International Conference on Distributed Computing Systems Workshops (ICDCSW'07)

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“…The eWatch work performs unsupervised machine learning to independently cluster sensor quantities from multiple sensors (i.e., light, skin temperature, microphone, accelerometer) into unlabeled contexts. These contexts are used to determine when to interupt users of incoming emails of varying priority levels [24]. We see this grouping of measurements into unlabeled contexts to be a very useful first step.…”

Section: Related Workmentioning

confidence: 99%

Intuitive Network Applications: Learning for Personalized Converged Services Involving Social Networks

Dinoff¹,

Ho²,

Hull³

et al. 2007

JCP

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The convergence of the wireline telecom, wireless telecom, and internet networks and the services they provide offers tremendous opportunities in services personalization. We distinguish between two broad categories of personalization systems: recommendation systems, such as used in advertising, and life- style assisting systems, which attempt to customize or specialize services to an individual’s needs, preferences, and habits. The Privacy-Conscious Personalization (PCP) framework, developed previously at Bell Labs, uses a high-speed rules engine to enable rich life-style assisting personalization. During network-hosted information sharing and call processing, the PCP framework can be used to interpret a combination of incoming requests, user data, and user preferences in order to provide contextaware, requester-targeted, and preferences-driven responses to those requests (e.g., deciding whether to share a user’s location with a given requester, what to show as the enduser’s availability to a given requester, where to forward an incoming call). This paper describes key aspects of a new initiative at Bell Labs, called Intuitive Network Applications (INA), which aims to combine human factors and automated learning techniques, in order to gather the user data and preferences needed for PCP-enabled personalization, with minimal disruption to the user. A particular focus of the paper is on life-style assisting capabilities for applications that involve the interaction of an end-user with her social network, i.e., family, friends, colleagues, customers, etc. The paper describes (i) key requirements, (ii) a high-level architectural framework, and (iii) some specific directions currently under exploration for filling out the framework.

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“…eWatch communicates wirelessly using a SMARTM Bluetooth module and an infrared data port for control of devices such as a television. A previous version of eWatch based on different hardware is described in [9].…”

Section: Related Workmentioning

confidence: 99%