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.
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