You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors 
Chapter Title:  Humenberger et al. 
1. Abstract of Cited Paper (BIB001):  This paper presents a recently developed dynamic stereo vision sensor system and its application for fall detection towards safety for elderly at home. The system consists of (1) two optical detector chips with 304×240 event-driven pixels which are only sensitive to relative light intensity changes, (2) an FPGA for interfacing the detectors, early data processing, and stereo matching for depth map reconstruction, (3) a digital signal processor for interpreting the sensor data in real-time for fall recognition, and (4) a wireless communication module for instantly alerting caring institutions. This system was designed for incident detection in private homes of elderly to foster safety and security. The two main advantages of the system, compared to existing wearable systems are from the application's point of view: (a) the stationary installation has a better acceptance for independent living comparing to permanent wearing devices, and (b) the privacy of the system is systematically ensured since the vision detector does not produce real images such as classic video sensors. The system can actually process about 300 kevents per second. It was evaluated using 500 fall cases acquired with a stuntman. More than 90% positive detections were reported. We will show a live demonstration during ISCAS2012 of the sensor system and its capabilities.

Literature Review:
In 2012 a bio-inspired stereo vision fall detection system was introduced by Humenberger et al. BIB001 . The system design is described in BIB002 and it utilizes two optical detector chips, a field-programmable gate array (FPGA), a digital signal processor (DSP) and a wireless communication module. The optical chips capture video frames. The FPGA creates the input data for the DSP by calculating 3D representations of the environment. The DSP is loaded with a neural network that is used for classification purposes. Falls are divided into 4 states or phases pre-fall, critical, post-fall, and recovery phase. To run the experiments the hardware was mounted on the top corners of a room in order to monitor the subjects of interest. The trial results are 90% of fall detection rate for all networks, and 97%-98% for the best network.