Optical fall detection with asynchronous temporal-contrast vision sensors for independently living older people

Abstract

Several studies have presented different issues of an ageing population including the need of enhancing care systems for older people using smart technologies. Falling accidents have a significant impact on healthy life expectancy and are a major problem among independently living older people. This paper presents a solution of the fall detection problem utilizing bio-inspired asynchronous temporal-contrast sensors and neural networks, realizing automated, robust, reliable and unobtrusive fall-detection. A noise reduction scheme suited to the unique nature of the sensor is presented, enabling their use in various applications in addition to fall detection. The process of transforming raw sensor output to a suitable neural network input is also described, along with the neural network creation process, including structure selection, training data assembly, and training algorithm selection for a truly large-scale network.