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:  WHY THE KINECT 
1. Abstract of Cited Paper (BIB001):  How good are cheap depth cameras, namely the Microsoft Kinect, compared to state of the art Time-of- Flight depth cameras? In this paper several depth cameras of different types were put to the test on a variety of tasks in order to judge their respective performance and to find out their weaknesses. We will concentrate on the common area of applications for which both types are specified, i.e. near field indoor scenes. The characteristics and limitations of the different technologies as well as the concrete hardware implementations are discussed and evaluated with a set of experimental setups. Especially, the noise level and the axial and angular resolutions are compared. Additionally, refined formulas to generate depth values based on the raw measurements of the Kinect are presented. 
2. Abstract of Cited Paper (BIB002):  Elderly suffers from injuries or disabilities through falls every year. With a high likelihood of falls causing serious injury or death, falling can be extremely dangerous, especially when the victim is home-alone and is unable to seek timely medical assistance. Our fall detection systems aims to solve this problem by automatically detecting falls and notify healthcare services or the victim’s caregivers so as to provide help. In this paper, development of a fall detection system based on Kinect sensor is introduced. Current fall detection algorithms were surveyed and we developed a novel posture recognition algorithm to improve the specificity of the system. Data obtained through trial testing with human subjects showed a 26.5% increase in fall detection compared to control algorithms. With our novel detection algorithm, the system conducted in a simulated ward scenario can achieve up to 90% fall detection rate. 
3. Abstract of Cited Paper (BIB003):  An embedded system for fully automatic fall detection.A method to achieve reliable fall detection with low false alarm ratio.An algorithm with low computational demands for person extraction in depth images.A freely available database for evaluation of fall detection, consisting of both accelerometric and depth data. Since falls are a major public health problem in an aging society, there is considerable demand for low-cost fall detection systems. One of the main reasons for non-acceptance of the currently available solutions by seniors is that the fall detectors using only inertial sensors generate too much false alarms. This means that some daily activities are erroneously signaled as fall, which in turn leads to frustration of the users. In this paper we present how to design and implement a low-cost system for reliable fall detection with very low false alarm ratio. The detection of the fall is done on the basis of accelerometric data and depth maps. A tri-axial accelerometer is used to indicate the potential fall as well as to indicate whether the person is in motion. If the measured acceleration is higher than an assumed threshold value, the algorithm extracts the person, calculates the features and then executes the SVM-based classifier to authenticate the fall alarm. It is a 365/7/24 embedded system permitting unobtrusive fall detection as well as preserving privacy of the user. 
4. Abstract of Cited Paper (BIB004):  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. 
5. Abstract of Cited Paper (BIB005):  The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance. 
6. Abstract of Cited Paper (BIB006):  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. 
7. Abstract of Cited Paper (BIB007):  A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person's vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over time. The second stage uses an ensemble of decision trees to compute a confidence that a fall preceded on a ground event. Evaluation was conducted in the actual homes of older adults, using a combined nine years of continuous data collected in 13 apartments. The dataset includes 454 falls, 445 falls performed by trained stunt actors and nine naturally occurring resident falls. The extensive data collection allows for characterization of system performance under real-world conditions to a degree that has not been shown in other studies. Cross validation results are included for standing, sitting, and lying down positions, near (within 4 m) versus far fall locations, and occluded versus not occluded fallers. The method is compared against five state-of-the-art fall detection algorithms and significantly better results are achieved. 
8. Abstract of Cited Paper (BIB008):  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:
The Kinect camera is a human motion tracking peripheral which was first developing for the Xbox 360 console BIB007 . It provides full-body 3D motion capture, facial recognition, and voice recognition capabilities BIB001 . It has been used in various systems, and the Kinect has been yet the first camera which combines the RGB color camera with structure-light depth camera into one single camera  . A Kinect is a sensor as other sensor have its advantages and disadvantages BIB003 BIB004 BIB008 BIB007  BIB009 BIB002 BIB005 . Many authors and programmer make the Kinect camera their first choice because it has been yet the first camera which combines all the following advantages: • Kinect combines multiple streams: since the Kinect camera combines the RGB color, depth, skeleton, infrared, body index, and audio into one single camera  , make the Kinect the first available camera in all these streams. • Kinect give RGB-D images: containing the depth information and the visual RGB information which takes the advantages of the RGB 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  . Combining RGB and depth information can dramatically improve the classification accuracy BIB009 which will help the developers to improve the performance of their system. • Suitable to the indoor application: with a comparison to another depth camera, it was found that the depth technique in Kinect is more suitable to the indoor application  . And that what makes the Kinect-based fall detection system suitable to be used in hospital and the care homes. • Safe sensor: the infrared (IR) laser in the Kinect uses a type 2 infrared light which is safe to use on the human, not like most of the laser scanners which use the type 1 laser that is dangerous to eyes when no protecting has been worn  . And this will ensure the safety of the user from any system using Kinect as the acquisition sensor. • Low obtrusive: all the camera including Kinect are low obtrusive not like the wearable sensors which can be highly obtrusive BIB006 and uncomfortable during normal daily life activities. And by using the Kinect, researcher will ensure the comfortability to the physically disabled persons who already not comfortable with their disability. • Availability and the low cost: the Kinect is widely available and has a small price which is 150$ including the adapter BIB001  . And with the free proposed software, it makes this meager cost for a complete hardware and software FD system. • Preserves the person's privacy: if only depth images are used, it protects the person's privacy by producing a stickman display or even a grey image BIB004 . • Independent of external light conditions: using the infrared light, the Kinect sensor is capable of extracting the depth maps in dark rooms BIB004 . So, it can detect falls in the late evening or even in the nighttime. • Portable: Other reasons for choosing Kinect because it is portability and the easiest in hold it and change its place  . • Fast frame-rate: it could use for real-time because of the fast frame-rate (FPS at 30Hz) BIB004 . • Reasonable range: from 0.4m to 4.5m BIB007  . Which suit to use in bedrooms and home care rooms. • Accurate depth information 1mm error after calibration: after calibration, the reprojection error is 0.16 pixels and 0.17 pixels. These results show that the lenses, which are used in Kinect, are already perfect since a typical webcam has a re-projection error greater than 0.7 pixels. Next section reviews extensively the hardware and software of the Kinect, and how this camera does the skeleton tracking.