Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> FALL DETECTION USING SKELETON JOINTS AND FEATURES <s> 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. <s> BIB001 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> FALL DETECTION USING SKELETON JOINTS AND FEATURES <s> 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. <s> BIB002 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> FALL DETECTION USING SKELETON JOINTS AND FEATURES <s> The elderly population is increasing rapidly all over the world. One major risk for elderly people is fall accidents, especially for those living alone. In this paper, we propose a robust fall detection approach by analyzing the tracked key joints of the human body using a single depth camera. Compared to the rivals that rely on the RGB inputs, the proposed scheme is independent of illumination of the lights and can work even in a dark room. In our scheme, a pose-invariant randomized decision tree algorithm is proposed for the key joint extraction, which requires low computational cost during the training and test. Then, the support vector machine classifier is employed to determine whether a fall motion occurs, whose input is the 3-D trajectory of the head joint. The experimental results demonstrate that the proposed fall detection method is more accurate and robust compared with the state-of-the-art methods. <s> BIB003 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> FALL DETECTION USING SKELETON JOINTS AND FEATURES <s> 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. <s> BIB004
The success in human activity recognition remains dependent on the correspondence between the human activities and the used joints features. Indeed, several activities may correspond to the movements of only certain body joints. This fact also applicable to the FD which is included in the core building blocks of systems under the umbrella of automatic Human Activity Recognition (HAR). Tower this end, several recent studies BIB002 BIB001 BIB003 BIB004 used skeleton joints to detect different scenarios of fall. C. Lee and V. Lee BIB002 use the Kinect v1 camera as the input sensor and Microsoft Kinect SDK to get skeleton data for their FD system. They chose to track the hip center joint whose position and velocity are used to detect three scenarios: fall in open space from walking or standing, fall from lying in bed, and non-fall events. Their system achieved a 90% FD accuracy fall because they could not detect falls off the chair. Christopher, Li, and Chung BIB001 developed algorithms to detect fall and also using Microsoft Kinect v1. They used the positions of all the 20 joints offered by Kinect v1 to calculate the floor plane equation and average velocity to detect falls. Using all joints in their system can distinguish between falls and slowly lying down on the floor. However, their system has problems detecting falls in cases like when somebody jumps in front of the camera; when a person walks out of the Kinect's vision area, it is occasionally detected as a fall; etc. Also, this algorithm does not perform very well on stairs. In 2015, Bian et al. BIB003 propose a robust FD approach based on human body part tracking using a single depth camera. In their scheme, a pose-invariant Randomized Decision Tree (RDT) algorithm is proposed for the 3D body joint extraction to capture the fall motion. So, they extract the body joints using their own algorithm which applied to the depth frames not the skeleton frames which already offered by the Kinect as our work. And since the head and hip are the most visible body parts, they use body part classification to extract them. To test their method, the SVM classifier employed on fall, and some ADL (crouching down, standing up, sitting down, walking) to determine whether a fall motion occurs. The most recent related work in fall features was published in 2016 by Maldonado et al. BIB004 . The authors investigated feature selection to identify the features that distinguish most between fallen and non-fallen poses. They included three poses in their dataset: walking, sitting and fallen. They used the Kinect depth camera as the input sensor and preprocessed the input data using background subtraction to detect the difference cloud of points that represent the person. Afterward, from the cloud points, they calculated twenty features describing falls. Finally, they used two methods to select the most relevant features: Genetic Algorithm (GA) and Principal Component Analysis (PCA). GA produced six features that best identify falls: three features represent angles, two represent the bounding box, and one is a moment invariant. Overall, the above works used either a fixed set or all of the skeleton joints; however, none of them tried to determine the joints which are most effective in detecting various types of fall scenarios. Maldonado et al. BIB004 tested some features that can be extracted from depth data (not skeleton data). Researchers must investigate the 25 skeleton joints among those offered by Kinect v2 (SDK 2.0) as in Figure 4 .b and their features that most efficiently could detect various fall scenarios.