Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Camera-Based Sensing <s> In this paper, we introduce a passive-type walker using servo brakes referred to as RT Walker. RT Walker realizes several functions such as obstacles/steps avoidance function, path following function, gravity compensation function, variable motion characteristics function, etc., by controlling only servo brakes without using servo motors. These passive-type systems are dependable for using practically in real world environment, because of the passive dynamics with respect to the applied force/moment, simple structure, lightweight, and so on. However, the most serious problem of them is the falling accident of user, because the passive-type systems are lightweight and move easily based on the small force/moment applied by the user unintentionally. In this paper, we pay attention to a method for estimating the human state during the usage of the walker and propose a motion control algorithm for realizing a fall-prevention function based on its human state. We also implement the proposed control methods in RT Walker experimentally and illustrate the validity of them <s> BIB001 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Camera-Based Sensing <s> This paper presents the design, implementation and evaluation of a distributed network of smart cameras whose function is to detect and localize falls, an important application in elderly living environments. A network of overlapping smart cameras uses a decentralized procedure for computing inter-image homographies that allows the location of a fall to be reported in 2D world coordinates by calibrating only one camera. Also, we propose a joint routing and homography transformation scheme for multi-hop localization that yields localization errors of less than 2 feet using very low resolution images. Our goal is to demonstrate that such a distributed low-power system can perform adequately in this and related applications. A prototype implementation is given for low-power Agilent/UCLA Cyclops cameras running on the Crossbow MICAz platform. We demonstrate the effectiveness of the fall detection as well as the precision of the localization using a simulation of our sample implementation. <s> BIB002 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Camera-Based Sensing <s> Nowadays, Western countries have to face the growing population of seniors. New technologies can help people stay at home by providing a secure environment and improving their quality of life. The use of computer vision systems offers a new promising solution to analyze people behavior and detect some unusual events. In this paper, we propose a new method to detect falls, which are one of the greatest risk for seniors living alone. Our approach is based on a combination of motion history and human shape variation. Our algorithm provides promising results on video sequences of daily activities and simulated falls. <s> BIB003 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Camera-Based Sensing <s> This paper presents a novel video-based human fall detection system that can detect a human fall in real-time with a high detection rate. This fall detection system is based on an ingenious combination of skeleton feature and human shape variation, which can efficiently distinguish “fall-down” activities from “fall-like” ones. The experimental results indicate that the proposed human fall detection system can achieve a high detection rate and low false alarm rate. <s> BIB004 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Camera-Based Sensing <s> Fainting is the unwanted event occur amongst the senior citizens, patients or pregnant women. This event can cause physical injuries or even mental problems. In this paper, we proposed a simple faint detection algorithm apply into a thermal imaging camera to monitor the fainting event. In addition, this system is to allow the immediate treatment can be carried out for life saving purposes when there are people fainting. Experimental results on sample images show that the proposed surveillance system achieved high accuracy of 96.15% for poor lighting condition and 86.19% in indoor in detecting the human faint event. <s> BIB005 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Camera-Based Sensing <s> An increase in population of elderly people living in isolated environments, leads to the need for an automated monitoring system. Fall is one of the major reasons for death of elderly people. So fall detection is an essential part of an automated indoor monitoring system. Video based automatic fall detection is robust and more reliable than other fall detection methods. Most of the available video based fall detection mechanisms are based on the extraction of motion dynamic features like velocity and intensity gradient of the person in video. These methods are computationally intensive and less accurate. This paper presents an accurate and computationally less intensive approach to detect fall, using only the static features of the person such as aspect ratio and inclination angle. Experimental results show that this method is robust in detecting all kinds of fall. <s> BIB006 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Camera-Based Sensing <s> Robust detection of events and activities, such as falling, sitting, and lying down, is a key to a reliable elderly activity monitoring system. While fast and precise detection of falls is critical in providing immediate medical attention, other activities like sitting and lying down can provide valuable information for early diagnosis of potential health problems. In this paper, we present a fall detection and activity classification system using wearable cameras. Since the camera is worn by the subject, monitoring is not limited to confined areas, and extends to wherever the subject may go including indoors and outdoors. Furthermore, since the captured images are not of the subject, privacy concerns are alleviated. We present a fall detection algorithm employing histograms of edge orientations and strengths, and propose an optical flow-based method for activity classification. The first set of experiments has been performed with prerecorded video sequences from eight different subjects wearing a camera on their waist. Each subject performed around 40 trials, which included falling, sitting, and lying down. Moreover, an embedded smart camera implementation of the algorithm was also tested on a CITRIC platform with subjects wearing the CITRIC camera, and each performing 50 falls and 30 non-fall activities. Experimental results show the success of the proposed method. <s> BIB007
Fall accidents happen in fractions of seconds, typically between 0.45 and 0.85 s BIB004 . During the incident, the posture and shape of the faller change drastically. These sudden changes are key to determine if a fall occurred. Camera-based systems benefit from these patterns by monitoring the subject's posture and shape during and after a fall. Dealing with images consumes a considerable amount of processing power. The frame rate (images per second) of a video is usually 24 frames per second (FPS); on each frame, camera-based algorithms process images pixel by pixel. A common digital image can have 720 × 480 pixels, that is 345,600 pixels that must be analyzed in 41.6 milliseconds, which requires a great deal of computational power. To mitigate this computational cost, images are usually compacted and redundant pixels are discarded in a pre-processing stage. Camera-based fall detection systems use different approaches to detect falls. For example, some systems such as the ones described in BIB004 BIB001 are based on human skeleton. They are robust solutions but their computational cost is unattractive for real time applications. Others systems are based on simpler features (e.g., the falling angle, vertical projection histogram) BIB002 BIB006 . They are not as computationally intensive as the human skeleton based systems but they suffer from a high false alarm rate. Privacy issues are one of the biggest concerns for camera-based systems. Few solutions to alleviate this problem are known. In BIB007 , Ozcan et al. developed a fall detection system that utilizes a wearable embedded smart camera that is mounted on the waist of the SOI. The camera does not record the SOI's motion pictures but the environment's around him/her. Other possible solutions use infrared cameras BIB003 and thermal cameras BIB005 .