You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors 
Chapter Title:  Support Vector Machines 
1. Abstract of Cited Paper (BIB001):  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

Literature Review:
Support vector machines (SVMs) are a relatively new type of supervised machine learning algorithms, and they outperform many of the classic algorithms. In a two-class classification problem, the main goal is to create a model that places every new example in the correct class. SVMs algorithms try to solve this problem by taking the training examples into a higher dimension where they are linearly separable and can be assigned to a class with little uncertainty BIB002 . Binary class datasets that are linearly separable are easy to classify because the decision boundary of the two classes is just a straight line or plane that divides the feature space into two regions: class A and B. In SVMs, the input space is transformed into a higher dimensional space using a non-linear mapping. The idea is to take the instances from the original feature space where they are not linearly separable to a new feature space where they are. On this new space, a hyperplane (a straight line in 2 dimensions) is created, and it works as a decision boundary that separates the data; this boundary is also known as the maximum margin hyperplane. The training points that are closest to the decision boundary are called support vectors. The support vectors uniquely define the maximum-margin hyperplane for the learning problem. In this manner, support vector machines search for a maximum margin hyperplane to separate the data with the examples on the border called support vectors. Every new entry will be taken to this new space where it will be classified depending on the region it falls into BIB002 . Figure 3 shows an example of a decision boundary. The math involved in SVMs is extremely complex and therefore difficult to implement  . The steps in order to realize a SVM training algorithm are described in Algorithm 3. Calculating the maximal margin hyperplane can be achieved by solving Equation (1). Finding b, α i and the support vectors a(i) is a type of optimization problem known as constrained quadratic optimization. There are currently two approaches to solving the quadratic optimization problem: the primal form and the dual form. The former transforms the original equation into a form that can be solved by quadratic optimization methods. The latter rewrites the classification rule into its unconstrained dual form, where the problem depends only on the support vectors  BIB001 . In BIB001 , an analysis of both kinds of implementation is given. They concluded that for a dataset or matrix of a attibutes (columns) and n examples (rows), the training computational time complexity is equal to O(na 2 + a 3 ) for the primal form and O(an 2 + n 3 ) for the dual form. Table 2 compares the most relevant characteristics of the supervised learning algorithms reviewed.