Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT-BASED FALL DETECTION APPROACHES <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 APPROACHES <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> BIB002 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT-BASED FALL DETECTION APPROACHES <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> BIB003 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT-BASED FALL DETECTION APPROACHES <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 APPROACHES <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> BIB005 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT-BASED FALL DETECTION APPROACHES <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> BIB006 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT-BASED FALL DETECTION APPROACHES <s> A factor seriously endangers the people health is falling, particularly for patients and the elderly. Fall detection systems contribute in preventing the consequences of the late medical aid and injuries endangering the people health. The main problem within fall detection systems is how to correctly distinguish between a fall and the other daily activities. There are various types of fall detection systems each of which has different advantages and disadvantages. Wireless motion-sensor based systems such as accelerometer and gyroscope provide higher efficiency with lower limits. This study introduces a new fall detection method employing motion sensors in smart phones to collect data due to the ease of access and application. To provide high efficiency for people with various ages and conditions, this method also takes advantages of adaptivefuzzy neural networks for learning and inference. These methods correctly detect all 4 types of fall from 9 main daily activity groups. <s> BIB007
Main-body text is to write in fully (left and right) justified 11 pt. Times New Roman font with a 6pt. (paragraph) line spacing following the last line of each paragraph, but a 12pt. (paragraph) line spacing following the last paragraph. Do not indent paragraphs. Several recent well-known studies BIB001 BIB007 BIB002 BIB005 classified under vision-based approaches, use the Kinect for developing FD systems. This section reviews and analyzes those related Kinect-based fall detection methods. And then summarizes their disadvantages and limitations and proposed some solution to solve it. C. Lee and V. Lee BIB003 present a system to detect falls and notify healthcare services or the victim's caregivers to provide help. They use the Kinect camera v1 as the input sensor and Microsoft Kinect SDK that provides their FD system with skeleton data. They choose the hip center joint to be tracked and processed the hip data received by two functions-based centers of mass. The first function checking if the hip position\coordinates within a certain threshold distance from the floor. And the second separates the user's hip center velocity into two components: vertical and horizontal velocities and then checks if the combined scores for both the vertical and horizontal components exceed their pre-set overall threshold score. If both functions return a fall, then a fall is tentatively indicated. After that, their postural recognition algorithm is then applied to reduce the number of false positives (such as sitting on the floor with both legs folded behind, kneeling on the floor, squatting, bending down to wear shoes or tie shoelaces) returned by their FD algorithm. For this purpose, two main defining features were identified and checked against previously obtained skeletal data of postures to ensure their specificity. The first feature, user's Ankles have to be below the user's hip center. The second feature, one of the user's legs is either folded below his body, or one knee is significantly higher than the other. Their method detect falls 100% in certain situations (fall from a bed or in open space) with specificity rate of up to 90%, that means their overall FD accuracy rate is 95%. They concern to detect the three kinds of falls that frequently occur within the Home. These three kinds of falls are: falling from the bed, falling in open spaces (which their system detect), or falling off the chair and that fall is undetected by them due to the fall taking place in a low position; and reduced time required by the system to measure a significant increase in user's velocity while he is falling, hence resulting in the system not being able to detect the fall correctly. Falls off the chair is an essential fall scenario and one of our concerns for people who have a physical disability and using a chair most of the time for resting. In 2014, in an effort to reduce the number of false alarms by collecting more information, Kwolek and Kepski BIB004 added to the Kinect a wearable smart device containing accelerometer and gyroscope sensors; this intelligent device is worn near the pelvis region of the monitored person. They use a triaxial accelerometer to indicate both a potential fall and whether the person is in motion, and the Kinect camera v1 with OpenNI library acquire depth images to reduce the number of false alarms and employ it whenever it is only possible. Their FD system runs under Linux OS. First, the data acquisition from the wearable device and transmitted wirelessly via Bluetooth to the processing device; and the depth acquisition from the Kinect which is connected via USB to this device. Then median filtered the depth image to fill the holes and smooth it. After that, store it in the circular buffer for further feature extraction and continuously updates it. While the person in motion, the system will continue extracting the foreground through subtraction to determines the connected components\objects. If the scene changes, the depth reference image will update. Otherwise, a potential of fall is examined using accelerometric data. If the measured acceleration is higher than the assumed threshold value equal to 3g, then recognize potential of fall and remove all the connected components in a binary image except the largest one which represented the segmented person. Then from the depth image, extract the correspond v-disparity image to calculate the floor plane parameters. After that, the system calculates some depth features, and classify the image\frame using SVM classifier to trigger the fall alarm. Their algorithm achieves 98.33% accuracy, 100% sensitivity, and 96.67% specificity when using accelerometer and depth data, and 90% accuracy, 100% sensitivity and 80% specificity when using depth only which is the worst result compared to other techniques in their research. The 98.33% accuracy rate was obtained not by using the Kinect alone but also with the help of accelerometer and gyroscope sensors which is not acceptable because of the need to wear and carry various uncomfortable devices during normal daily life activities, the elderly may forget to wear such devices or determine not to be worn during the sleep which lack the ability of such detectors to detect a fall. In addition, they use foreground which needs to determine whether a foreground object actually is or is not a person, and in some cases, is or is not the person under the study. And when using the Kinect only, they achieved 80% specificity which means they could not avoid the false alarm by 20% (false alarm rate = 0.2). Le and Morel  present a novel FD system based on the Kinect v1 sensor. First, they compute the room floor plane using the Kinect's floor plane equation. Second, they extract the head and spine joints coordinates and convert them to floor coordinates. Then, they calculate the distance from the floor and velocity features. After that, they classify the frames using the SVM. From their results, they first obtained 83.56% accuracy, 91.12% sensitivity, and 76% specificity. And after they remove the fall-like samples (sit and lie down the ground) from the training data, their results get higher to 91% accuracy, 100% sensitivity, and 82% specificity. Finally, they detect a fall on a duration of time to solve the misclassification of "Lie down the floor" which frequently classified as a fall activity, and they get 98.35% accuracy, 100% sensitivity, and 96.7% specificity. The disadvantage of their work was the dataset that used for algorithm training which contains only nine scenarios: four falls, two falls-like, and three ADL (Activity of Daily Living). Forthermore, their scenarios performed by their subjects in a very specific way by marketing the ground using five points as shown in Figure 5 ; and in each scenario, the subject must follow these points and fall in a specific direction which make it less natural. That means, even when changing of the subject, the scenario will be performed at the same exact way. In 2015, Stone and Skubic BIB006 developed a two-stage FD system for detecting falls using the Kinect v1 in the homes of older adults. The first stage characterizes the vertical state of a 3D object for an individual frame using three features: the maximum height of the object, the height of the object's centroid, and the number of elements of the discretized (floor) plane. Then, using the vertical state of the tracked object over time series, it segments on ground events after filtering the vertical state time series using median and average filters. The second stage extracted five features from an on-ground event to generate confidence that a fall preceded it. Those features are minimum vertical velocity, maximum vertical acceleration, mean vertical velocity, occlusion adjusted change, and minimum frame-to-frame vertical velocity. Finally, using those five features and an ensemble of decision trees, fall confidence is computed for each on the ground event. As a preprocessing step, this system segments 3D foreground objects from each depth frame using dynamic background subtraction. When the falls are near the sensor and not significantly occluded, this system can achieve 98%, 70%, and 71% cross-validation accuracy detection of standing, sitting, and lying falls, respectively; however, when the falls are far to the sensor and significantly occluded, the system can achieve 79%, 58%, and 5% cross-validation accuracy detection of standing, sitting, and lying falls, respectively. They reached a shallow false alarm (only one per month) but the FD accuracy is not high enough especially for sitting and lying positions, and when the faller is far from the Kinect. The low accuracy when the faller is far because of the 320×240 depth resolution used in this system to keep the space required to store the data to a manageable. And the low accuracy detection of sitting because the presence of the chair used by the faller which is often part of or the entire chair will be identified as foreground; and that will result the fall. Finally, the low accuracy detection of sitting because of the significantly reduced fall motion. Existing FD systems using Kinect BIB003 BIB004 BIB006  differ in their performance. Some do not cover many fall types and scenarios; others have high false alarm rates when operating on particular 86 postures, yet others have low accuracy when the faller is far from the Kinect, or even trained with the less natural dataset. In addition, those trying to improve the FD rate use wearable sensors, which may hinder daily activities and make the person uncomfortable. Furthermore, the proposed systems' performance evidently depends on the features used. However, the feature differences (in nature and number) and the lack of a benchmark dataset hinders a systematic evaluation of the performance of existing systems. FD with the Kinect is a complicated process for which presently there is no an identical solution. As such, a FD method must: suits physically disabled people, relies solely on Kinect, and can determine various types of falls in different postures with a low false alarm and a high accuracy. Author suggests in Table 4 some solutions to avoid the preceding disadvantages of the literature studies as shown. Using the Kinect only (which will decrease the cost and unobtrusive) When using the Kinect only, they could not avoid the false alarm by 20% Using skeleton stream rather than depth which is better on person tracking and will reduce the false alarm rate The foreground needs to determine whether a foreground object is or isn't a person, and in some cases, is or is not the person under the study Use skeleton tracking from SDK (which track only the person and could determine the person under the study by using the tracked id) than foreground technique Le and Morel  (2014) Kinect camera v1 + Kinect SDK To solve the misclassification of fall-like which frequently classified as fall activity, they have to detect a fall in a duration of time which increases the computations Because they use the distance from the floor features, their system will consider the fall-like as fall. Investigate different features that do not detect the fall-like as fall The dataset scenarios perform in particular way which makes it less natural Using more natural dataset by allow the subject to perform the scenario on his adaptive way Using the newest Kinect version (v2) which has 512×424 pixels depth resolution Low accuracy detection of sitting because the presence of the chair used by the faller which is often part of or the entire chair will be identified as foreground which results in the fall Use skeleton tracking from SDK which tracks only the user rather than foreground technique which identify some objects as part of the user The low accuracy detection of lying because the significantly reduced fall motion Avoid it by skeleton tracking which only interest on the positions of the skeletal joints