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: Multimedia Internet of Things: A Comprehensive Survey 
Chapter Title:  D. HEALTH MONITORING 
1. Abstract of Cited Paper (BIB001):  There is a growing emphasis to find alternative non-traditional ways to manage patients to ease the burden on health care services largely fuelled by a growing demand from sections of population that is ageing. In-home remote patient monitoring applications harnessing technological advancements in the area of Internet of things (IoT), semantic web, data analytics, and cloud computing have emerged as viable alternatives. However, such applications generate large amounts of real-time data in terms of volume, velocity, and variety thus making it a big data problem. Hence, the challenge is how to combine and analyse such data with historical patient data to obtain meaningful diagnoses suggestions within acceptable time frames (considering quality of service (QoS)). Despite the evolution of big data processing technologies (e.g. Hadoop) and scalable infrastructure (e.g. clouds), there remains a significant gap in the areas of heterogeneous data collection, real-time patient monitoring, and automated decision support (semantic reasoning) based on well-defined QoS constraints. In this study, the authors review the state-of-the-art in enabling QoS for remote health care applications. In particular, they investigate the QoS challenges required to meet the analysis and inferencing needs of such applications and to overcome the limitations of existing big data processing tools. 
2. Abstract of Cited Paper (BIB002):  The maturity of healthcare IoT technology which connects medical devices and applications to healthcare IT systems through internet has driven the rapid growth of healthcare. In recent years, great effort has been spent to improve ocular disease screening and diagnosis using advanced image and data analysis techniques. However, the developed systems are not widely used because they are usually offline and separated from medical devices. In this paper, we introduce a platform that connects medical devices, patients, ophthalmologists, and intelligent ocular disease analysis systems through a cloud-based system. The platform is designed in a hybrid cloud pattern to offer both easy accessibility and enhanced security. The retinal fundus images and patients' personal data can be uploaded to the public cloud tier through multiple channels including retinal fundus cameras, web portals, mobile applications and APIs. The data will be transferred to the private cloud tier where automatic analysis and assessment will be performed using advanced pattern classification algorithms. Subsequently, the analysis report will be made available in the public tier so that patients can access their own report through mobile applications or web portals. Furthermore, patients with high risk of having ocular diseases will be referred to ophthalmologists. The platform helps to form an integrated ecosystem that enables an efficient and cost-effective way of ocular disease screening and monitoring, allowing early disease detection and intervention. 
3. Abstract of Cited Paper (BIB003):  Long-bone fractures such as femur fractures are very common in trauma centers. Robotic assisted fracture surgery (RAFS) can facilitate the minimally invasive surgery which reduces scarring, infection risk and long hospital stays. One important step in RAFS is to establish the coordinate system link between patient joint (rigidly connected with the robotic system) and an external tracking system. As X-ray fluoroscopic images are routinely used during the procedure, an automatic method is proposed to detect and localize landmarks on the tracking tool using live X-ray image. The proposed method uses combination of block detection, geometric model matching and principle component analysis. A successful rate of 91.3% is achieved after testing on 650 X-ray images and accuracy is within 0.5 mm. 
4. Abstract of Cited Paper (BIB004):  This paper presents a controllable robotic arm via the use of the Internet of Things (IoT). A nurse could control the robotic arm in a hospital to assist a doctor during a surgery and take care of the patient with the picking and placing tasks using the robotic arm. In this system, a smart phone will be used to control the robotic arm. An accelerometer and a gyroscope are used to capture the gestures and postures of the smart phone. The signals of the accelerometer and the gyroscope will be captured by an Android application and sent to a Raspberry Pi to control the robotic arm. By integrating the Internet of Things (IoT), a worldwide controllable robotic arm is achieved. The Android application is developed by using Android Studio. Python script is employed in the Raspberry Pi to develop a program that will be able to control the robotic arm and to receive the commands from the smart phone. A system with a kinematic model will be used in the Raspberry Pi to control the robotic arm. Besides, a video streaming from computer is implemented to monitor the robotic arm. The performance of the robot is investigated in term of latency of the system and IoT platform. 
5. Abstract of Cited Paper (BIB005):  Individual surgeons rely on residency programs as their main conduit for developing the necessary soft skills needed to succeed and excel in the operating room. One critical skill requiring subjective (qualitative) learning involves navigating through varying soft and hard tissues by hand, and, most importantly, understanding how medical instruments respond under these conditions. During residency, students learn the specific methods of the teaching surgeons, using specific instruments chosen by both the administrators of the residency programs and medical staff. The availability of practice time is limited not by the interest of the resident, but due to limitations in resources (e.g. high cost of cadaver parts, program insurance, and available contact time with patients in the operating room). By using data extraction during training, teaching surgeons can determine how well participants learn a subjective skill. Without data extraction, residents lack quantifiable numbers to guide them. Such values would verify correct techniques and highlight areas for improvement. In the end, the effectiveness of surgical training, mastery of soft skills, and overall surgical experience directly helps or harms patient outcomes [1]. We intend to coordinate with research scientists to develop Internet of Things (IoT) solutions incorporating existing data for training and real-time feedback. This data, which pertains to bicortical drilling conditions, will provide teaching surgeons and their surgical residents evaluation tools beyond those currently available. 
6. Abstract of Cited Paper (BIB006):  In this article, we explore the relevance and contribution of new signals in a broader interpretation of multimedia for personal health. We present how core multimedia research is becoming an important enabler for applications with the potential for significant societal impact. 
7. Abstract of Cited Paper (BIB007):  Internet of Things (IoT) is a set of massively emerging technologies that aim to make our lives better. One of the most suitable application domains is healthcare and wellbeing, where IoT technologies can enhance living environments (ELE). IoT applications are deployed into an IoT ecosystem, which is supported by a specific architecture. The usual IoT architecture model has three different layers (Acquisition, Network and Application), where all the different IoT elements are organized. This architecture has been designed for general purposes, but the deployment of IoT healthcare applications supported by this architecture model exhibits important drawbacks. In particular, this architecture model does not consider IoT devices coexisting with wearables devices, which hinders the design of smart habitats. In this paper we present an IoT-aware architectural model for smart habitats with special emphasis on healthcare domain. The new architecture adds a fourth layer to the traditional IoT architecture, describing the software artefacts and tips needed to support context-aware habitats through the interaction between wearable computing devices and IoT devices. The main goal of the IoT architecture presented is to enhance living environments for elderly people. 
8. Abstract of Cited Paper (BIB008):  It is believed that small cells will play a significant role in future networks (e.g., 5G) to meet the high user requirements in traffic volume, frequency efficiency, and energy and cost reduction. The small cell network is a new paradigm for the successful deployment of mobile healthcare (m-health) applications. It can enhance the medical Quality of Service (m-QoS) and medical Quality of Experience (m-QoE) for indoor, outdoor, and vehicular m-health end-users (e.g., patients and healthcare professionals). The three major contributions of the paper are: 1) the investigation of the impact of small cell heterogeneous networks for medical video streaming as an example of m-health application in the uplink direction. 2) introducing an important m-health use case (i.e., an ambulance scenario) along with its system model and technical requirements, and 3) the provision of a network performance analysis for medical video sequences affected by packet losses and different networks as a benchmark to enable researchers to test their medical video quality evaluation algorithms. The results of our proposed scenario show through simulation that the m-QoS key performance indicators (e.g., packet loss, throughput, and delay) in a mobile small cell-based ambulance scenario outperform the traditional macrocell network scenario. Hence, improving the network performance of the paramedic staff inside the ambulance can facilitate in robust delivery of emergency m-health services, such as real-time remote consultations and diagnosis.

Literature Review:
Multimedia data provides the medium to communicate, monitor, and cooperate with various aspects of daily life at numerous levels of granularity across various applications, which in terms of health is known as personal health media. Incorporating personal health media in one device to measure everything using IoT is the features of the future health system, as shown in Fig. 8 . For this purpose, highly elaborative personal health data features are required. Boll et al. BIB007 presented a logical device layer-based architecture for mapping multimedia signals from various devices to the smallest elaborative unit named as primary health feature. Mapping is also performed to fuse multiple features and deploy advanced analytics to gain minor health details. Zafra et al. BIB008 addressed the issue of coexistence wearable devices for e-health with traditional three-layered IoT architecture and proposed a pervasive layered architecture to integrate M2M communication between e-health wearable and IoT devices. Shah et al. BIB002 highlight the challenges in analyzing big data generated from remote health monitoring and decision making by comparison with the patient's history. The authors also provided details of various QoS driven Cloud of Things (CoT) based data processing for remote health applications. Rehman et al. BIB009 proposed 5G small cell network deployment in an ambulance for efficient uplink medical video streaming to enhance the medical QoS (m-QoS). Ultrasound video is considered as uplink traffic. Medical QoS performance is analyzed on the basis of throughput, delay, and Packet Loss Ratio (PLR). Results depict that implementing small cell networks augments the performance of the system. However, improvements are still required for deploying remote medical health systems as packets drop in critical medical condition is not tolerable. BIB002 ), robotic ambulance  , X-ray for robotic surgery ( BIB004 , BIB005 ), biomedical training for surgeons BIB006 and ocular diseases detection using smartphones BIB001 , BIB003 . All these applications could be integrated using cloud service to be available for online and offline access. Sudden cardiac arrest is a heart condition in which heart stops due to lack of oxygen leading to death if immediate Automated External Defibrillator (AED) is not given. AED is a small device for untrained or minimal trained personnel to use it until the first aid arrives. Samani and Zhu  designed an intelligent robotic ambulance named AmbuBot to provide AED to save a life. AmbuBot is equipped with a GSM module for remote connectivity and high-resolution cameras for Lane Keeping System (LKS) to track lanes and prevent accidents. The system can be further extended for multiple conditions, body sensors, and multiple robots for collaborative work. Robot-Assisted Fracture Surgery (RAFS) is revolutionizing the medical health care by reducing infection risk during surgery and perfectly manipulate each bone fragment into the perfect position. The coordination system is in need between the detected bone track and an external robotic tool to exact position the broken bone. Automatic tool detection for surgery assistance based on computer vision X-ray images is proposed in BIB004 . Block detection, geometric model matching, and principal component analysis are utilized to achieve a 91% success rate. The robotic arm can extend the perfection provided by 3D positioning. Smartphone controlled Raspberry Pi based robotic arm is designed to assist the doctor in surgery BIB005 . The platform offers builtin compatibility of WiFi, camera and other sensors. Surgical skills and effectiveness directly affect the patient's health. To minimize the harm to the patient, surgeons should be highly trained with subjective skills. To overcome this problem, research scientists, along with the medical Doctors in BIB006 have designed a biomedical IoT data extraction technique to train surgeons and provide real-time feedback on their skills efficiently. The proposed biomedical trainer architecture comprises virtual images, medical images, patients data, and journal data. The performance of the surgeon is evaluated by capturing the surgeon's technique, visualizing the data, and comparison with the benchmark. Multimedia data features in IoT further benefits the field of medical health by retinal cataract detection technique for remote area patients. To achieve this, authors in BIB001 proposed a design to implement a microscopic lens over a low-cost smartphone camera and real-time detection by FeedForward Neural Network (FFNN) trained the algorithm. Yin et al. BIB003 enhance the system to detect multiple ocular diseases. The proposed automatic architecture includes retinal images of data acquisition using a fundus camera. Cloud platform stores patient history, process, analyze and extract features of stored images. The reports are generated after analysis, and corresponding ophthalmologists are referred. The proposed methodology aims to reduce the workload on medical specialists and save time.