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...

Dual-axis manual control: Performance degradation, axis asymmetry, crossfeed, and intermittency

Vehicle control tasks require simultaneous control of multiple degrees-of-freedom. Most multi-axis human-control modeling is limited to the modeling of multiple fully independent single axes. This paper contributes to the understanding of multi-axis control behavior and draws a more realistic and complete picture of dual-axis manual control....

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...

The MatchNMingle dataset: A novel multi-sensor resource for the analysis of social interactions and group dynamics in-the-wild during free-standing conversations and speed dates

We present MatchNMingle, a novel multimodal/multisensor dataset for the analysis of free-standing conversational groups and speed-dates in-the-wild. MatchNMingle leverages the use of wearable devices and overhead cameras to record social interactions of 92 people during real-life speed-dates, followed by a cocktail party. To our knowledge,...