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:  Introduction 
1. Abstract of Cited Paper (BIB001):  As aging progresses, fall accident of the person using the walker is an acute problem. It is necessary to know the situation of the userpsilas fall to prevent it. In this paper, we propose a method for estimating the userpsilas fall by modeling of the user in real time as a solid body link model, and pay attention to the center of gravity of the model. We also propose a method for controlling a passive intelligent walker to prevent the userpsilas fall according to the support polygon and the walking characteristic of the user. We experimented with passive intelligent walker in which we implement the proposed fall prevention control and show the effectiveness of the proposal method. 
2. Abstract of Cited Paper (BIB002):  In the present work, we introduce Fallarm, a pervasive fall prevention solution suitable for hospitals and care facilities, as well as for home settings. We applied a multifaceted intervention strategy based on closed-loop information exchange between proactive and reactive methods: comprehensive assessment protocols determine the individuals' risk of falling; an innovative device continuously monitors subjects' activities, and it provides patients with constant feedback about their actual risk. Thus, it increases their awareness; simultaneously, it realizes measures to prevent adverse events, and it reports any incident and aims to reduce the level of injury. As a result, our solution offers a comprehensive strategy for the remote management of a person's risk of falling 24 hours a day, enabling many vulnerable people to remain living independently. In this paper, we detail the architecture of our system, and we discuss the results of an experimental study we conducted to demonstrate the applicability of Fallarm in both clinical and home settings. 
3. Abstract of Cited Paper (BIB003):  In principle, activity recognition can be exploited to great societal benefits, especially in real-life, human centric applications such as elder care and healthcare. This article focused on recognizing simple human activities. Recognizing complex activities remains a challenging and active area of research and the nature of human activities poses different challenges. Human activity understanding encompasses activity recognition and activity pattern discovery. The first focuses on accurate detection of human activities based on a predefined activity model. An activity pattern discovery researcher builds a pervasive system first and then analyzes the sensor data to discover activity patterns. 
4. Abstract of Cited Paper (BIB004):  Robust detection of events and activities, such as falling, sitting, and lying down, is a key to a reliable elderly activity monitoring system. While fast and precise detection of falls is critical in providing immediate medical attention, other activities like sitting and lying down can provide valuable information for early diagnosis of potential health problems. In this paper, we present a fall detection and activity classification system using wearable cameras. Since the camera is worn by the subject, monitoring is not limited to confined areas, and extends to wherever the subject may go including indoors and outdoors. Furthermore, since the captured images are not of the subject, privacy concerns are alleviated. We present a fall detection algorithm employing histograms of edge orientations and strengths, and propose an optical flow-based method for activity classification. The first set of experiments has been performed with prerecorded video sequences from eight different subjects wearing a camera on their waist. Each subject performed around 40 trials, which included falling, sitting, and lying down. Moreover, an embedded smart camera implementation of the algorithm was also tested on a CITRIC platform with subjects wearing the CITRIC camera, and each performing 50 falls and 30 non-fall activities. Experimental results show the success of the proposed method. 
5. Abstract of Cited Paper (BIB005):  Providing accurate and opportune information on people's activities and behaviors is one of the most important tasks in pervasive computing. Innumerable applications can be visualized, for instance, in medical, security, entertainment, and tactical scenarios. Despite human activity recognition (HAR) being an active field for more than a decade, there are still key aspects that, if addressed, would constitute a significant turn in the way people interact with mobile devices. This paper surveys the state of the art in HAR based on wearable sensors. A general architecture is first presented along with a description of the main components of any HAR system. We also propose a two-level taxonomy in accordance to the learning approach (either supervised or semi-supervised) and the response time (either offline or online). Then, the principal issues and challenges are discussed, as well as the main solutions to each one of them. Twenty eight systems are qualitatively evaluated in terms of recognition performance, energy consumption, obtrusiveness, and flexibility, among others. Finally, we present some open problems and ideas that, due to their high relevance, should be addressed in future research. 
6. Abstract of Cited Paper (BIB006):  The mobile application is capable of detecting possible falls for elderly, through the use of special sensors. The alert messages contain useful information about the people in danger, such as his/her geo location and also corresponding directions on a map. In occasions of false alerts, the supervised person is given the ability to estimate the value of importance of a possible alert and to stop it before being transmitted. This paper describes system for monitoring and fall detection of ELDERLY PEOPLE using triaxial accelerometer together with ZigBee transceiver to detect fall of ELDERLY PEOPLE. The Accidental Fall Detection System will be able to assist careers as well as the elderly, as the careers will be notified immediately to the intended person. This fall detection system is designed to detect the accidental fall of the elderly and alert the careers or their loved ones via Smart-Messaging Services (SMS) immediately. This fall detection is created using microcontroller technology as the heart of the system, the accelerometer as to detect the sudden movement or fall and the Global System for Mobile (GSM) modem, to send out SMS to the receiver

Literature Review:
According to  , more than 1.6 million U.S. adults are treated for fall-related injuries in emergency rooms each year. The most common consequences of these injuries are fractures, loss of independence, and even death. Two of the most common causes of falls are due to aging and obstacles in home environment. As people get older, their bodies go through multiple physical changes making them more fragile, and more prone to falls. For instance, vision deteriorates over time hindering one's capability to explore their surroundings and identify tripping obstacles. In order to minimize the effects of aging as the human physicality decays medications are prescribed. Paradoxically, this increases a person's probability to fall since some drugs reduce mental alertness  . A person's living environment is filled with potential fall hazards; which is the reason why most falls happen at home  . Slippery floors, clutter, poor lighting, unstable furniture, obstructed ways and pets are common hazards inside a home BIB001 . References    suggest conducting a thorough analysis of the house and identifying the possible causes of a fall. From this analysis, a preventative checklist can be created to minimize the risk of a fall. An example checklist might be the following: • Install handrails on stairs and steps. • Remove tripping objects (such as papers, books, clothes, and shoes) from stairs and walkways. • Put grab bars inside and next to the tub or shower, and next to your toilet. • Improve lighting. • Wear sturdy shoes both inside and outside the house. • Train pets. Dogs are more likely to cause a fall than cats. There exist other ways to prevent fall-related accidents besides home modifications. Motivating patients to exercise, being aware of medication side effects and taking care of vision problems are common methods to avert a fall. Teaching adults how to prevent risk factors is crucial to avoid future falls, since most of them believe that falls are part of aging and nothing can be done about it  . Falls have unique patterns and characteristics that can be exploited to detect and predict them. For example, the falling speed rises proportionally with the inertial characteristics of the falling subject. The acceleration is biased to its negative component along the axis perpendicular to the ground. There is also a change in the direction after a fall followed by a period of the subject's inactivity BIB003 . Fall detection (FD) and fall prevention (FP) systems have been studied for over a decade, the former the most investigated. They share many things in common, for example, they both employ sensing devices to accomplish their tasks. Additionally, they make sense of the collected data through computer vision, data mining and machine learning techniques. These systems have to overcome many challenges in order to design and implement an effective FD or FP system. Some of the issues they face are the following: obtrusiveness, occlusion, multiple people in the scene (camera-based system), aging (kinematics characteristics change over time), privacy, computational complexity, cost, noise, defining a threshold (threshold-based systems) BIB007 BIB005 BIB004 . These and other design issues will be covered in detail in Section 3. Fall detection and prevention systems have been designed using either external sensors or wearable sensors. External sensors are deployed in the vicinity of the subject of interest (SOI), and wearable sensors are attached to the SOI BIB006 . There have been also other approaches that use a combination of both types of sensors, known as hybrid systems. This type of systems will not be covered in this review. Camera-based sensors are perhaps the most common types of sensors used in external sensing. One or multiple cameras are placed in predefined fixed locations where the SOI will perform his/her daily activity. The main drawback of these sensors is their inability to track the user out of the cameras' range of visibility. Another important fact about external sensing is its high cost, as multiple sensors must be purchased to increase the system's coverage BIB006 . Proximity sensors are another typical example of the external sensors used in fall detection systems. Many of these sensors are usually attached to a walking-aid device, such as a cane or a walker. A fall is detected by measuring sudden changes in the SOI's movements and his/her distance from the proximity sensors. One of the problems these sensors have is their short proximity range; if a person steps away from the walker, the SOI will be out of the sensors' range, which can be misinterpreted as a fall. Also, some proximity sensors can be significantly expensive BIB002 . Wearable sensors are an alternative to external sensing. They are frequently employed in FD and FP systems. Wearable sensors are attached to the SOI's body, eliminating the space limitation imposed by external sensing. In addition, wearable sensors are generally cheaper than external sensors. The main disadvantage of wearable sensors is their high level of obtrusiveness BIB006 . Accelerometers are a type of wearable sensors that are widely used in fall detection systems. They are cheap and can be worn on different parts of the body. They are usually embedded in other devices such as watches, shoes, belts, etc. With this single sensor most of the SOI's movement characteristics can be extracted and used to detect falls. Fall prevention systems also take advantage of external sensors and wearable sensors. Similarly, different motion characteristics are extracted from the collected data, which are used to estimate the likelihood of a fall and alert the user in real time. This paper surveys the state of the art in FD and FP systems, including qualitative comparisons among various studies. It aims to serve as a point of reference for biomedical engineers or anyone interested in conducting investigations on fall-related systems. Section 2 looks closely to the model used by FD and FP systems. Section 3 is dedicated to describe the most important aspects to consider in the design of these systems. Section 4 presents FD systems in detail. Section 5 introduces FP systems. Section 6 presents a qualitative evaluation of FP and FD systems. Finally, Section 7 concludes the paper.