Micro sensor based eye movement detection and neural network based sensor fusion and fault detection and recovery

A person with one eye missing, through various reasons, may suffer psychologically as well as physically. The loss of an eye can be solved cosmetically by an ocular implant. This artificial eye appears natural, but it is static. To let the artificial eye have the same natural movement as the real eye, an ocular system is developed. The artificial eye is mounted onto a tiny servomotor. The whole system shall be able to sense the real eye movement and control the motor to drive the artificial eye to the desired position. A tiny infrared sensor array is used for this study. This paper describes an approach of using the artificial neural network to do the sensor fusion to detect the eye movement. Two types of neural networks are used for the sensor fusion and sensor fault detection and recovery respectively. Usually the sensor fusion relies on the model of the system, however, sometimes it is not possible to get an accurate model of the system, or one or several of the parameters of the system may be unknown or partially known. In addition, there may be measurement inaccuracies associated with the sensors. In this case, the conventional method may not have a good performance. An artificial neural network can learn the characteristic of a non-linear, non-modeled system through training samples. Then during the real application, the sensor signal can be used to feed the network and obtain the desired output. Using the micro sensor array to detect the eye movement we carried out an experimental study. The sensor data is amplified and digitized then sent to the computer. Two-layer neural networks are trained by the data samples. First, the trained network is used for sensor fusion, and then two neural networks are used to detect the sensor failure and recover the faulty data respectively. Experimental studies with soft sensor failure and hard sensor failure are included. The main part of this paper deals with the network training method and further considerations