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Richard Harper

Researcher at Lancaster University

Publications -  201
Citations -  9409

Richard Harper is an academic researcher from Lancaster University. The author has contributed to research in topics: Computer-supported cooperative work & Mobile phone. The author has an hindex of 47, co-authored 200 publications receiving 8972 citations. Previous affiliations of Richard Harper include University of Surrey & National Health Service.

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Proceedings ArticleDOI

Demonstrating the accuracy of an in-hospital ambulatory patient monitoring solution in measuring respiratory rate

TL;DR: Clinical testing conducted to evaluate the accuracy of Aingeal, a wireless in-hospital patient monitor, in measuring respiration rate via impedance pneumography demonstrates comparable performance of the Aingealing device in measuringrespiration rate with a well-accepted and widely used alternative method.
Proceedings ArticleDOI

Glancephone: an exploration of human expression

TL;DR: It was found that Glacephones were used to draw attention to oneself, not to encourage better control of interruption and greeting sequences, and Bourdieu's concepts of habitus and relatedly, distinction are proposed as explanatory tools for this and other evidence about expression enabled by mobile and other technologies of communication.
Journal ArticleDOI

The ‘interrogative gaze’:Making video calling and messaging ‘accountable’

TL;DR: In this paper, the authors identify salient properties of how talk about video communication is organised interactionally, and how this interaction invokes an implied order of behaviour that is treated as "typical" and "accountably representative" of video communication.
Proceedings ArticleDOI

'Safety in numbers': calculation and document re-use in knowledge work

TL;DR: Some aspects of the everyday use and re-use of engineering documents in the practical accomplishment of everyday knowledge work are outlined as the first stage in considering how these activities can be technologically supported.
Proceedings Article

Towards GPU Utilization Prediction for Cloud Deep Learning

TL;DR: This paper demonstrates that it is possible to predict DL workload GPU utilization via extracting information from its model computation graph and proposes a prediction engine to proactively determine the GPU utilization of heterogeneous DL workloads without the need for in-depth or isolated online profiling.