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: Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey 
Chapter Title:  KINECT SOFTWARE AND SKELETON TRACKING 
1. Abstract of Cited Paper (BIB001):  This paper presents a new and efficient algorithm for complex human activity recognition using depth videos recorded from a single Microsoft Kinect camera. The algorithm has been implemented on videos recorded from Kinect camera in OpenNI video file format (.oni). OpenNI file format provides a combined video with both RGB and depth information. An OpenNI specific dataset of such videos has been created containing 200 videos of 8 different activities being performed by different individuals. This dataset should serve as a reference for future research involving OpenNI skeleton tracker. The algorithm is based on skeleton tracking using state of the art OpenNI skeleton tracker. Various joints and body parts in human skeleton have been tracked and the selection of these joints is made based on the nature of the activity being performed. The change in position of the selected joints and body parts during the activity has been used to construct feature vectors for each activity. Support vector machine (SVM) multi-class classifier has been used to classify and recognize the activities being performed. Experimental results show the algorithm is able to successfully classify the set of activities irrespective of the individual performing the activities and the position of the individual in front of the camera. 
2. Abstract of Cited Paper (BIB002):  Computational modeling of human behavior has become a very important field of computer vision. Gesture recognition allows people to interact with machines in a natural way without the use of dedicated I/O devices. This paper presents a simple system that can recognize dynamic and static gestures using the depth map and the higher level output (skeleton and facial features) provided by a Kinect sensor. Two approaches are chosen for the recognition task: the Dynamic Time Warping Algorithm is used to recognize dynamic gestures, while a Bayesian classifier is used for the static gestures/postures. In contrast with some specialized methods presented in the literature, the current approach is very generic and can be used with minimal modification for recognizing a large variety of gestures. As a result, it can be deployed in a multitude of fields from security (monitoring rooms and sending alarm signals), medicine (helping people with physical disabilities) to education and so on. The tests results show that the system is accurate, easy to use and highly customizable. 
3. Abstract of Cited Paper (BIB003):  RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms.

Literature Review:
For the Kinect software, after the Microsoft has released the Xbox360, some companies provide unofficial free libraries and SDK for the Kinect such as CL NUI Platform, OpenKinect\Libfreenect, OpenNI, and PCL. In 2011, the Microsoft released the official Kinect SDK BIB004 . The Kinect SDK 2.0 enables developers to create applications that support voice and gesture recognition, using Kinect sensor technology on computers running Windows 8, Windows 8.1, and Windows Embedded Standard 8. It also includes Application Programming Interfaces (APIs), device interfaces and code samples. It also provides free tools which can be used for detecting and tracking the body\skeleton and the head of a person BIB003 . For more about Kinect free libraries see BIB004 . Fall detection approach as most of the computer vision applications for Human Activity Recognition (HAR) which recognize human activities through skeleton tracking by representing body parts as joints. They exploit the fact that body features calculated from a 3D skeleton increase robustness across persons and can lead to higher performance BIB001 . Skeleton tracking can be offered from the previous different libraries such as unofficial OpenNI (Open Natural Interaction) and official Kinect SDK and different Kinect versions such as v1 and v2 BIB004 . But each has its advantages and disadvantages in skeleton tracking. Kinect v1 can recognize up to six users and tracks up to two users in details BIB003 and detect 20 joints per people. While Kinect v2, can recognize and tracks up to six users in details and detect 25 joints per people BIB004 . For the Kinect libraries, Kinect SDK 1.8 and older track the full body include head, hands, feet, clavicles and can calculate 3D positions of 20 joints per people (Figure 4 .a) BIB003 . While SDK 2.0, do the same but with 25 joints per people (Figure 4 .b). On another hand, OpenNI does not support the track of head, hands, feet, clavicles but support the hands only mode BIB004 and calculate 3D positions and rotation of 15 joints per people BIB002 . Table 3 shows the difference between the two versions of Kinect SDK 1.8 and older, and 2.0 in skeleton tracking.