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Computational Interaction$
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Antti Oulasvirta, Per Ola Kristensson, Xiaojun Bi, and Andrew Howes

Print publication date: 2018

Print ISBN-13: 9780198799603

Published to Oxford Scholarship Online: March 2018

DOI: 10.1093/oso/9780198799603.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 27 October 2021

Input Recognition

Input Recognition

(p.65) 3 Input Recognition
Computational Interaction

Otmar Hilliges

Oxford University Press

Sensing of user input lies at the core of HCI research. Deciding which input mechanisms to use and how to implement them such that they work in a way that is easy to use, robust to various environmental factors and accurate in reconstruction of the users intent is a tremendously challenging problem. The main difficulties stem from the complex nature of human behavior which is highly non-linear, dynamic and context dependent and can often only be observed partially. Due to these complexities, research has turned its attention to data-driven techniques in order to build sophisticated and robust input recognition mechanisms. In this chapter we discuss the most important aspects that constitute data-driven signal analysis approaches. The aim is to provide the reader with an overall understanding of the process irrespective of the exact choice of sensor or machine learning algorithm.

Keywords:   Input sensing, machine learning, signal processing, classification, regression, deep learning

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