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:  DISCUSSION AND CONCLUSION 
1. Abstract of Cited Paper (BIB001):  Falls are the leading cause of injury and death among older adults in the US. Computer vision systems offer a promising way of detecting falls. The present paper examines a fall detection and reporting system using the Microsoft Kinect sensor. Two algorithms for detecting falls are introduced. The first uses only a single frame to determine if a fall has occurred. The second uses time series data and can distinguish between falls and slowly lying down on the floor. In addition to detecting falls, the system offers several options for reporting. Reports can be sent as emails or text messages and can include pictures during and after the fall. A voice recognition system can be used to cancel false reports. 
2. Abstract of Cited Paper (BIB002):  In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summarize the emergence of `ambient-assisted living” (AAL) tools for older adults based on ambient intelligence paradigm. We will summarize the state-of-the-art AAL technologies, tools, and techniques, and we will look at current and future challenges. 
3. Abstract of Cited Paper (BIB003):  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. 
4. Abstract of Cited Paper (BIB004):  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. 
5. Abstract of Cited Paper (BIB005):  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. 
6. Abstract of Cited Paper (BIB006):  A factor seriously endangers the people health is falling, particularly for patients and the elderly. Fall detection systems contribute in preventing the consequences of the late medical aid and injuries endangering the people health. The main problem within fall detection systems is how to correctly distinguish between a fall and the other daily activities. There are various types of fall detection systems each of which has different advantages and disadvantages. Wireless motion-sensor based systems such as accelerometer and gyroscope provide higher efficiency with lower limits. This study introduces a new fall detection method employing motion sensors in smart phones to collect data due to the ease of access and application. To provide high efficiency for people with various ages and conditions, this method also takes advantages of adaptivefuzzy neural networks for learning and inference. These methods correctly detect all 4 types of fall from 9 main daily activity groups. 
7. Abstract of Cited Paper (BIB007):  In this paper we are interesting in knowing which features provide useful information for detecting a fall and how the set of selected characteristics impact the performance of detection. Then we define a large set of possible features, which are extracted from a cloud of points of a person by the kinect device, some of features were used in previous work, and we propose to add and evaluate the effect of using 3D moment invariants translation, scale an rotation, and other geometric characteristics. Two experiments are carried out to analyze the effect of using two different subset of features, one of them selected by a Genetic Algorithm and the second by Principal Component Analysis (PCA). The obtained results suggest that the success of detection of fall depends on the selected features, and the genetic algorithm is a good technique to select them, when compared with PCA.

Literature Review:
In this survey paper, author introduces a brief background about the most popular device-based fall detection approaches in general. Also overviews the acquisition sensor (the Kinect camera), its hardware and software. Then showed the skeleton tracking using Kinect. Also, the reasons that make researchers choose the Kinect camera and its official SDK to detect the fall of the physically-disabled cane users are explained. The related works on FD that used Kinect are overviewed; their limitation\disadvantages and how to solve them. Followed by a discussion of the existing Kinect-based fall datasets. Finally, the skeleton joints and features that used for FD are overviewed. There are still many issues and challenges that motivate the development of new Kinect-based FD technique to improve the accuracy under more realistic conditions. Some of these issues for society and the health care system that result from increasing disabling population such as increase in health care costs, shortage of caregivers, dependency and larger impact on society BIB003 . 90 And the rest issues were disadvantages in the literature techniques of FD BIB004 BIB005 BIB006  . These techniques have designed to monitor the healthy people or home alone elderly, but not specialty for the people with physical disabilities who use canes. And some of them BIB004 , was lacking to detect some postures such as falls off the chair which is an important posture to be detected for the disabled people who use chair frequently to rest. While other BIB005 has a high false alarm rate. And some  trained their system with the less natural dataset. Yet some BIB006 have low accuracy for sitting and lying positions, and when the faller is far from the Kinect. But the main issue in FD is how to distinguish between a fall and other daily activities which are very similar to a fall BIB007 . As for FD datasets recorded using the Kinect camera, our study revealed that many of them BIB004 BIB005 BIB006 BIB001 , differ in terms of their coverage, size stream types, and availability. This also highlights a need for a fall dataset that is available to researchers, that covers many fall types and scenarios, and most importantly, that is recorded in all the streams provided by the Kinect camera to provide for the needs of different techniques. Furthermore, for the skeleton-based fall detection related works BIB004 BIB007 BIB002 BIB008 , they used either a fixed set or all of the skeleton joints; however, none of them tried to investigate the joints which are most effective in detecting various types of fall scenarios. The research on FD can be extended from dataset aspects and the proposed method aspects, and the implementation aspects: 1. From the dataset aspects, the existing datasets could be improved by feeding more scenarios in dark rooms or late evenings. And with more female and male subjects who have different body shapes and sizes. 2. From the proposed method aspects, they can be improved by solving the false positive scenarios, provide it by a postural recognition algorithm as done in BIB004 which could be applied after the detection to test if they are real fall or just a false positive. 3. From the implementation aspects, the proposed methods must develop a completed FD system; and feed it with voice recognition algorithm in order to validate the fall and reduce the false positive as done in BIB002 .