Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Chen et al. <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> BIB001 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Chen et al. <s> Falls in elderly is a major health problem and a cost burden to social services. Thus automatic fall detectors are needed to support the independence and security of the elderly. The goal of this research is to develop a real-time portable wireless fall detection system, which is capable of automatically discriminating between falls and Activities of Daily Life (ADL). The fall detection system contains a portable fall-detection terminal and a monitoring centre, both of which communicate with ZigBee protocol. To extract the features of falls, falls data and ADL data obtained from young subjects are analyzed. Based on the characteristics of falls, an effective fall detection algorithm using tri-axis accelerometers is introduced, and the results show that falls can be distinguished from ADL with a sensitivity over 95% and a specificity of 100%, for a total set of 270 movements. <s> BIB002
A human fall detection system using a computer vision approach is introduced in BIB001 . The solution is capable of detecting fall-related events in real time using skeleton features and human shape variations. The system is able to extract the human posture and reduce the computational burden by using a 2D model instead of a complicated 3D one. The human contour is partitioned into triangular meshes by using the constrained delaunay triangulation algorithm  . The skeleton (a spanning tree) is acquired by running the well-known graph traversal algorithm Depth-first search (DFS) on the center of the triangular meshes. The Douglas-Peucher algorithm is used to reduce the number of pixel in the human contour, effectively decreasing the computational complexity. A distance map is used to calculate the distance between two skeletons. A fall is detected if the user's motion does not change within a certain period of time. The system is able to obtain a high detection accuracy (90.9%) while maintaining a low false alarm rate. Chen et al. proposed a wireless fall detector using accelerometers BIB002 . The system consists of two modules, a fall detection terminal and a remote one; they can communicate with each other through wireless communication. The fall detection terminal works without the remote one. The remote terminal is used to receive and store kinematic characteristics and raise alarms. The solution employs an inertial sensor (an accelerometer), a microcontroller and wireless SoC (System on chip). A threshold-based algorithm is used to detect falls. Five tests were performed: forward fall (FF), backward fall (BF), left-side fall (LF), right-side fall (RF), and ADLs. The results of the study showed a 97% of recall and 100% of specificity.