Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Bilgin et al. <s> We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds. <s> BIB001 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Bilgin et al. <s> Fall detection technology is critical for the elderly people. In order to avoid the need of full time care giving service, the actual trend is to encourage elderly to stay living autonomously in their homes as long as possible. Reliable fall detection methods can enhance life safety of the elderly and boost their confidence by immediately alerting fall cases to caregivers. This study presents an algorithm of fall detection, which detects fall events by using data-mining approach. The authors' proposed method performs detection in two steps. First, it collects the wireless sensor network (WSN) data in stream format from sensor devices. Second, it uses k-nearest neighbour algorithm, that is, well-known lazy learning algorithm to detect fall occurrences. It detects falls by identifying the fall patterns in the data stream. Experiments show that the proposed method has promising results on WSN data stream in detecting falls. <s> BIB002 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Bilgin et al. <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> BIB003
This system BIB002 uses a data mining approach to detect falls. It is based on wireless sensor networks (WSNs). A WSN is a network where all of its nodes (also known as motes) are deployed in predefined locations gathering data about their surrounding. Each mote sends the collected information wirelessly (RF) to a base station or sink where the information is processed. Motes are usually equipped with multiple types of sensors (e.g., thermometers, barometers, microphones, accelerometers, etc.). Motes are constrained in energy, computational power and bandwidth. This work only employs the accelerometer, and it is only capable of detecting a fall when a person is standing. The sensor is placed on the waist of the subject. It uses a k-NN (Section II) approach to detect if a fall has occurred. The accuracy obtained on the conducted experiments was 89.4% with a recall of 100% and precision of 85%. The basic idea behind this solution BIB003 is to create an autonomous system that is able to provide quick and accurate real-time responses to critical events like a fall, while preserving computational resources. The system is not only able to detect falls but also to classify non-critical events such as sitting and lying down. Their solution is based on a modified version of the histogram of oriented gradients (HOGs) algorithm BIB001 . When a fall occurs edge orientations in a frame vary drastically and extremely fast, as a result of this subsequent frames get blurred. A dissimilarity distance is computed between two frames, and if it is greater than a predefined threshold a fall is detected. A microsoft LifeCam camera mounted on the center of the SOI's pelvis was used to capture the images. One of the drawbacks of the system is its lack of auto exposure adjustment in the camera. False alarms may be raised if the scenery changes.