Kalman filter improvement for gyroscopic mouse movementsmoothing

The Kalman filter is formally an algorithm used to produce estimation of a random variable based on measurement containing noise observed over time. This paper discusses Kalman filter capabilities to smooth noisy data obtained from electro-mechanical gyroscopes and accelerometers. Data produced by the sensor unit contain two types of noise: noise induced by the electronic (both properties of the electro-mechanical nature of the electronic and noise introduced by digitization) and noise induced by human i.e., noise caused by shivering of human hand. The goal is to smooth the data produced by sensor unit movement to make human gestures more distinguishable. Optimal design and slight modification of the Kalman algorithm is discussed in this paper.