Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT-BASED FALL DETECTION DATASETS <s> This paper presents a novel fall detection system based on the Kinect sensor. The system runs in real-time and is capable of detecting walking falls accurately and robustly without taking into account any false positive activities (i.e. lying on the floor). Velocity and inactivity calculations are performed to decide whether a fall has occurred. The key novelty of our approach is measuring the velocity based on the contraction or expansion of the width, height and depth of the 3D bounding box. By explicitly using the 3D bounding box, our algorithm requires no pre-knowledge of the scene (i.e. floor), as the set of detected actions are adequate to complete the process of fall detection. <s> BIB001 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT-BASED FALL DETECTION DATASETS <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> KINECT-BASED FALL DETECTION DATASETS <s> This paper presents a new and efficient algorithm for complex human activity recognition using depth videos recorded from a single Microsoft Kinect camera. The algorithm has been implemented on videos recorded from Kinect camera in OpenNI video file format (.oni). OpenNI file format provides a combined video with both RGB and depth information. An OpenNI specific dataset of such videos has been created containing 200 videos of 8 different activities being performed by different individuals. This dataset should serve as a reference for future research involving OpenNI skeleton tracker. The algorithm is based on skeleton tracking using state of the art OpenNI skeleton tracker. Various joints and body parts in human skeleton have been tracked and the selection of these joints is made based on the nature of the activity being performed. The change in position of the selected joints and body parts during the activity has been used to construct feature vectors for each activity. Support vector machine (SVM) multi-class classifier has been used to classify and recognize the activities being performed. Experimental results show the algorithm is able to successfully classify the set of activities irrespective of the individual performing the activities and the position of the individual in front of the camera. <s> BIB003 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT-BASED FALL DETECTION DATASETS <s> 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. <s> BIB004 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT-BASED FALL DETECTION DATASETS <s> 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. <s> BIB005 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT-BASED FALL DETECTION DATASETS <s> RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms. <s> BIB006
Since 2011, there have been different datasets which were taken by the Kinect camera. Cai et al. BIB006 surveyed 46 existing RGB-D benchmark datasets among which 20 are well elaborated. These RGB-D datasets contain the depth information and the visual (RGB) information, and the Kinect sensor can acquire them. They have divided it into five categories depending on the facilitated computer vision applications. These categories include object detection and tracking, human activity analysis, object and scene recognition, SLAM (Simultaneous Localization and Mapping) and hand gesture analysis. In human activity analysis category, Cai et al. in BIB006 overviewed the UR Fall Detection dataset created by the University of Rzeszow in 2014 BIB003 . This dataset was created by Kwolek and Kepski to detect and recognize the human fall. It was recorded using two Kinect cameras and corresponding accelerometric data in rooms such offices, classrooms, etc.The first camera was parallel to the faller within a 1m distance from the floor, and the second camera was on the room's roof within a 2.5m distance from the floor to cover all the room. This dataset contains 70 sequences: 30 sequences represent the fall situations which was recorded by the two cameras, and 40 sequences represent the ADL which recorded by only one camera. This dataset was performed by five healthy subjects who acted two types of fall: fall from standing and fall from sitting on a chair. Some images from this dataset are shown in Figure 6 .a. Figure 6 : Samples of previous Kinect-based fall detection datasets: (a) Image samples of RGB and depth images from UR fall detection dataset BIB004 , (b) Image samples of depth images performed in apartments of older adults from BIB005 , (c) Image samples of infrared images from the dataset in BIB001 , and (d) Image samples of fall scenario from dataset in  C. Lee and V. Lee BIB002 used a preliminary dataset to optimize their enhanced FD algorithm. This dataset includes 34 fall and non-fall events of 10,479 frames skeletal data captured by Kinect. Stone et al. BIB005 present a method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage FD system. They deployed a Kinect camera and a computer in the 13 elderly resident's homes at an independent living facility. The 16 residents' ages were from 67 to 97. Seven were male, and nine were female. The 3,339 days of continuous data containing 454 (near and far) falls include 14 standing, five sitting, and two lying fall scenarios. Also, it contains three scenarios of fall-like. The Kinect is placed above the front door on a small shelf a few inches below the ceiling in distance height 2. Mastorakis and Makris BIB001 attached the Kinect to a tripod at a 2.04m distance from the floor and at no farther than 7m from the area of possible fall. They capture 184 videos include: 48 falls, 12 slow falls, 48 lying activities on the floor, 32 seating activities, 12 slow activities and 32 picking up an item from the floor. The fall types include: forward, backward and sideways. Eight subjects performed their dataset, two of them performed in slow motion to simulate the behavior of an older adult. Their data was in infrared streams. An example of this dataset shown in Figure  6 .c. Le and Morel  present a method to detect fall using skeleton data and SVM technique. They evaluate their proposed method using dataset recorded using Kinect v1. Their dataset contains nine activities (four falls, two fall-like, and three ADL) performed in particular ways. Fall activities include back, front, right, and left falls; and fall-like includes sit and lie down actions; while ADL activities include walk, sit down the bed, pick an object. The nine activities\scenarios performed twice by six subjects with 20 to 35 ages in a 3x3m room with a bed; the room marked by five points on the ground, so the subjects can follow when performing the scenarios. Finally, they end with fall dataset contain 108 videos. An example is shown in Figure 6 .d. Overall, there were different fall datasets recorded using the latest technology Kinect [11-13, 29, 33] . As summarized in Table 5 , they differ regarding their coverage, size stream types, and availability. This table also highlights a need for a fall dataset that is available to researchers, that suit the cane users, that covers many fall types and scenarios, and most importantly, that recorded in all the streams provided by the Kinect camera to provide for the needs of different techniques. 