Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Humenberger et al. <s> In this paper, we present a bio-inspired, purely passive, and embedded fall detection system for its application towards safety for elderly at home. Bio-inspired means the use of two optical detector chips with event-driven pixels that are sensitive to relative light intensity changes only. The two chips are used as stereo configuration which enables a 3D representation of the observed area with a stereo matching technique. In contrast to conventional digital cameras, this image sensor delivers asynchronous events instead of synchronous intensity or color images, thus, the privacy issue is systematically solved. Another advantage is that stationary installed fall detection systems have a better acceptance for independent living compared to permanently worn devices. The fall detection is done by a trained neural network. First, a meaningful feature vector is calculated from the point clouds, then the neural network classifies the actual event as fall or non-fall. All processing is done on an embedded device consisting of an FPGA for stereo matching and a DSP for neural network calculation achieving several fall evaluations per second. The results evaluation showed that our fall detection system achieves a fall detection rate of more than 96% with false positives below 5% for our prerecorded dataset consisting of 679 fall scenarios. <s> BIB001 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Humenberger et al. <s> 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. <s> BIB002
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.