Wiring considerations in analog VLSI systems, with application to field-programmable networks
Wiring considerations in analog VLSI systems, with application to field-programmable networks
Accurate, fast fall detection using posture and context information
Proceedings of the 6th ACM conference on Embedded network sensor systems
Accurate, fast fall detection using posture and context information
Proceedings of the 6th ACM conference on Embedded network sensor systems
Human Body Posture Classification by a Neural Fuzzy Network and Home Care System Application
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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This paper presents an optimized implementation of a neural network for fall detection using a Silicon Retina stereo vision sensor. A Silicon Retina sensor is a bio-inspired optical sensor with special characteristics as it does not capture images, but only detects variations of intensity in a scene. The data processing unit consists of an event-based stereo matcher processed on a field programmable gate array (FPGA), and a neural network that is processed on a digital signal processor (DSP). The initial network used double-precision floating point arithmetic; the optimized version uses fixed-point arithmetic as it should be processed on a low performance embedded system. We focus on the performance optimization techniques for the DSP that have a major impact on the run-time performance of the neural network. In summary, we achieved a speedup of 48 for multiplication, 39.5 for additions, and 194 for the transfer functions and, thus, realized an embedded real-time fall detection system.