Keywords
UIST2.0 Archive - 20 years of UIST
Back
Back to keywords index

machine

answering machine

In Proceedings of UIST 2003
Article Picture

TalkBack: a conversational answering machine (p. 41-50)

machine learning

In Proceedings of UIST 2007
Article Picture

Eyepatch: prototyping camera-based interaction through examples (p. 33-42)

Abstract plus

Cameras are a useful source of input for many interactive applications, but computer vision programming is difficult and requires specialized knowledge that is out of reach for many HCI practitioners. In an effort to learn what makes a useful computer vision design tool, we created Eyepatch, a tool for designing camera-based interactions, and evaluated the Eyepatch prototype through deployment to students in an HCI course. This paper describes the lessons we learned about making computer vision more accessible, while retaining enough power and flexibility to be useful in a wide variety of interaction scenarios.

In Proceedings of UIST 2007
Article Picture

Robust, low-cost, non-intrusive sensing and recognition of seated postures (p. 149-158)

Abstract plus

In this paper, we present a methodology for recognizing seatedpostures using data from pressure sensors installed on a chair.Information about seated postures could be used to help avoidadverse effects of sitting for long periods of time or to predictseated activities for a human-computer interface. Our system designdisplays accurate near-real-time classification performance on datafrom subjects on which the posture recognition system was nottrained by using a set of carefully designed, subject-invariantsignal features. By using a near-optimal sensor placement strategy,we keep the number of required sensors low thereby reducing costand computational complexity. We evaluated the performance of ourtechnology using a series of empirical methods including (1)cross-validation (classification accuracy of 87% for ten posturesusing data from 31 sensors), and (2) a physical deployment of oursystem (78% classification accuracy using data from 19sensors).

In Proceedings of UIST 2010
Article Picture

Gestalt: integrated support for implementation and analysis in machine learning (p. 37-46)

Abstract plus

We present Gestalt, a development environment designed to support the process of applying machine learning. While traditional programming environments focus on source code, we explicitly support both code and data. Gestalt allows developers to implement a classification pipeline, analyze data as it moves through that pipeline, and easily transition between implementation and analysis. An experiment shows this significantly improves the ability of developers to find and fix bugs in machine learning systems. Our discussion of Gestalt and our experimental observations provide new insight into general-purpose support for the machine learning process.

machine vision

state machine

In Proceedings of UIST 2006
Article Picture

SwingStates: adding state machines to the swing toolkit (p. 319-322)

time machine

In Proceedings of UIST 1997
Article Picture

TimeSlider: an interface to specify time point (p. 43-44)