portada Selection of Psychophysiological Features Across Subjects for Classifying Workload Using Artificial Neural Networks (en Inglés)

Selection of Psychophysiological Features Across Subjects for Classifying Workload Using Artificial Neural Networks (en Inglés)

Formato

Libro Físico

Autor

Trevor I. Laine

Editorial

Biblioscholar

Idioma

Inglés

N° páginas

166

Encuadernación

Tapa Blanda

Dimensiones

24.6x18.9x0.9 cm

Peso

0.31 kg.

ISBN13

9781288289097

Categorías

Educación

Selection of Psychophysiological Features Across Subjects for Classifying Workload Using Artificial Neural Networks (en Inglés)

Trevor I. Laine (Autor) · Biblioscholar · Tapa Blanda

Libro Nuevo Importado

Envío: 16 a 22 días háb.

$ 55.00$ 27.50

-50%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 50 unidades

$ 27.50

Llega entre el 15 Sep y el 25 Sep a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Selection of Psychophysiological Features Across Subjects for Classifying Workload Using Artificial Neural Networks (en Inglés)"

The issue of pilot workload is important to the United States Air Force because pilot overload or task saturation leads to decreases in mission effectiveness. Additionally, in the most extreme cases, pilot overload may lead to the loss of aircraft and crewmember lives. Current research efforts are utilizing psychophysiological data including electroencephalography (EEG), cardiac, eye-blink, and respiration measures in attempt to identify workload levels. The primary focus of this effort is to determine if a single parsimonious set of psychophysiological features exists for accurately classifying workload levels between multiple test subjects. To accomplish this objective, the signal-to-noise (SNR) saliency measure is used to determine the usefulness of psychophysiological features in feedforward artificial neural networks (ANNs). The SNR saliency measure determines the saliency, or relative value, of a feature by comparing it to a feature of injected noise. For this effort, 36 psychophysiological features were derived from the data collected as each subject completed simulated crewmember tasks using the Multi-Attribute Task Battery developed by NASA. These tasks were randomly presented to the subjects in blocks with three distinct levels: low, medium, and an overload level in which subjects could not complete all tasks.