Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Feature Extraction <s> This chapter introduces the reader to the various aspects of feature extraction covered in this book. Section 1 reviews definitions and notations and proposes a unified view of the feature extraction problem. Section 2 is an overview of the methods and results presented in the book, emphasizing novel contributions. Section 3 provides the reader with an entry point in the field of feature extraction by showing small revealing examples and describing simple but effective algorithms. Finally, Section 4 introduces a more theoretical formalism and points to directions of research and open problems. <s> BIB001 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Feature Extraction <s> Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization <s> BIB002
Feature extraction is the process by which relevant characteristics or attributes are identified from the collected data BIB001 . In fall-related systems the acceleration magnitude or the angular magnitude are frequently extracted. Features have to be cautiously picked in order to get a more descriptive and usually smaller output dataset. The number of features in a dataset has a direct impact on the dataset's descriptive power. The more features a dataset has, the more expressive it is. However, finding meaningful relationships among the instances and the class can be more difficult because the feature space grows exponentially with the number of features. Feature extraction is also known as dimensionality reduction. Raw data are usually filled with meaningless information. By selecting only the features that best describe the input data and discarding redundant features, the size of the dataset is reduced. This reduction usually lowers the work of the subsequent modules BIB002 . Although the size of the dataset is usually shrunk (space embedding procedures), there are some methods that enlarge it (non-linear expansions, feature discretization) or even leave it unchanged (e.g., signal enhancement, normalization, standardization) BIB001 . Feature extraction is commonly divided into two categories: feature construction and feature selection. They are described next.