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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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Input Recognition

Input Recognition

Chapter:
(p.65) 3 Input Recognition
Source:
Computational Interaction
Author(s):

Otmar Hilliges

Publisher:
Oxford University Press
DOI:10.1093/oso/9780198799603.003.0004

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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