Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> THE STATE OF ART ON FALL DETECTION <s> Fall detection of the elderly is a major public health problem. Thus it has generated a wide range of applied research and prompted the development of telemonitoring systems to enable the early diagnosis of fall conditions. This article is a survey of systems, algorithms and sensors, for the automatic early detection of the fall of elderly persons. It points out the difficulty to compare the performances of the different systems due to the lack of a common framework. It then proposes a procedure for this evaluation. <s> BIB001 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> THE STATE OF ART ON FALL DETECTION <s> Fall detection is a major challenge in the public health care domain, especially for the elderly, and reliable surveillance is a necessity to mitigate the effects of falls. The technology and products related to fall detection have always been in high demand within the security and the health-care industries. An effective fall detection system is required to provide urgent support and to significantly reduce the medical care costs associated with falls. In this paper, we give a comprehensive survey of different systems for fall detection and their underlying algorithms. Fall detection approaches are divided into three main categories: wearable device based, ambience device based and vision based. These approaches are summarised and compared with each other and a conclusion is derived with some discussions on possible future work. <s> BIB002 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> THE STATE OF ART ON FALL DETECTION <s> Since falls are a major public health problem among older people, the number of systems aimed at detecting them has increased dramatically over recent years. This work presents an extensive literature review of fall detection systems, including comparisons among various kinds of studies. It aims to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations. Challenges, issues and trends in fall detection have been identified after the reviewing work. The number of studies using context-aware techniques is still increasing but there is a new trend towards the integration of fall detection into smartphones as well as the use of machine learning methods in the detection algorithm. We have also identified challenges regarding performance under real-life conditions, usability, and user acceptance as well as issues related to power consumption, real-time operations, sensing limitations, privacy and record of real-life falls. <s> BIB003 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> THE STATE OF ART ON FALL DETECTION <s> 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. <s> BIB004 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> THE STATE OF ART ON FALL DETECTION <s> Falls are a major cause of fatal injury for the elderly population. To improve the quality of living for seniors, a wide range of monitoring systems with fall detection functionality have been proposed over recent years. This article is a survey of systems and algorithms which aim at automatically detecting cases where a human falls and may have been injured. Existing fall detection methods can be categorized as using sensors, or being exclusively vision-based. This literature review focuses on vision-based methods. <s> BIB005
FD system is an assistive device whose primary objective is to alert when a fall event has occurred. Several studies reviewed existing FD systems. Mubashir et al. BIB002 , Igual et al. BIB003 , Zhang et al. BIB005 , and Noury et al. BIB001 surveyed FD systems using different review criteria for fall types and used devices. Mubashir et al. BIB002 , introduced different types of falls, including falls from walking, standing, standing on supports (e.g., ladders), sleeping, lying in bed, or sitting on a chair. In addition, they classified FD approaches into three categories based on the used device (see Figure 1 ): wearable device-based, ambience sensor-based, and camera (vision) based approaches. Igual et al. BIB003 identified three other fall types (forward, backward and sideways), and they classified the reviewed FD systems into two types: context-aware systems and wearable devices. Zhang et al. BIB005 classified the reviewed FD methods into either non-vision sensor-based, or exclusively vision sensor-based. They reviewed vision-based methods only which they classified into three categories based on their camera type: single RGB camera-based, 3D-based using multiple RGB cameras, and 3D-based using depth cameras. In contrast, Noury et al. BIB001 classified the reviewed FD methods in terms of their technique, either analytical or machine learning methods. In addition, they obtain 20 fall scenarios\types from the literature researchers. Their scenarios/types were categorized as: backward fall (both legs straight or with knee flexion), forward fall, lateral fall to the right, lateral fall to the left, syncope and neutral. Based on the above studies BIB002 BIB003 BIB005 BIB001 , the advantages and disadvantages of existing FD approaches which classified into three categories depending on the used sensor are summarized in Figure 2 . Based on this summary, this study focus on the third class of approaches (vision-based) and, in particular, the Kinect skeleton-based approach. Indeed, ambient sensor-based approaches sense the pressure of everything in and around the person and generating too much false alarms BIB002 . As for wearable sensor-based approaches, they may have limited acceptance by the users for three main reasons: the highly obtrusive nature of wearable sensors, their incurred discomfort during normal, daily life activities BIB004 , and their numerous false alarms. In contrast, a Kinect skeletonbased approach (in the third category) offers multiple advantages. So, in this paper considers only a Kinect as acquisition sensor for its advantages which this paper will focus on and discuss later in this section after presented fall scenarios and the challenges related to fall detection. 