This physiological data was collected from pilot/copilot pairs in and out of a flight simulator. It was collected to train machine-learning models to aid in the detection of pilot attentive states.
The benchmark training set is comprised of a set of controlled experiments collected in a non-flight environment, outside of a flight simulator. The test set (abbreviated LOFT = Line Oriented Flight Training) consists of a full flight (take off, flight, and landing) in a flight simulator.
The pilots experienced distractions intended to induce one of the following three cognitive states:
Channelized Attention (CA) is the state of being focused on one task to the exclusion of all others. This is induced in benchmarking by having the subjects play an engaging puzzle-based video game.
Diverted Attention (DA) is the state of having one’s attention diverted by actions or thought processes associated with a decision. This is induced by having the subjects perform a display monitoring task. Periodically, a math problem showed up which had to be solved before returning to the monitoring task.
Startle/Surprise (SS) is induced by having the subjects watch movie clips with jump scares.
For each experiment, a pair of pilots (each with its own crew ID) was recorded over time and subjected to the CA, DA, or SS cognitive states. The training set contains three experiments (one for each state) in which the pilots experienced just one of the states. For example, in the experiment labelled CA, the pilots were either in a baseline state (no event) or the CA state. The test set contains a full flight simulation during which the pilots could experience any of the states (but never more than one at a time).
Each sensor operated at a sample rate of 256 Hz. Please note that since this is physiological data from real people, there will be noise and artifacts in the data.