Classifying Human Manual Control Behavior Using LSTM Recurrent Neural Networks

This article discusses a long short-term memory (LSTM) recurrent neural network that uses raw time-domain data obtained in compensatory tracking tasks as input features for classifying (the adaptation of) human manual control with single- and double-integrator controlled element dynamics. Data from two different experiments were used to train...

Effects of Preview Time in Manual Tracking Tasks

In manual control tasks, preview of the target trajectory ahead is often limited by poor lighting, objects, or display edges. This paper investigates the effects of limited preview, or preview time, in manual tracking tasks with single- and double-integrator controlled element dynamics. A quasi-linear human controller model is used to predict...