Efeitos da experiência e das demandas cognitivas sobre a carga de trabalho mental durante a condução de automóvel: métodos de pupilometria
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Data
2023-03-17
Autores
Silva, Gustavo de Andrade
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Universidade Estadual Paulista (Unesp)
Resumo
Com a variedade de dispositivos eletrônicos disponíveis no ambiente interno do carro, aumentou-se a preocupação com a possibilidade desses equipamentos desviarem a atenção do motorista da tarefa de conduzir um veículo no trânsito. Os smartwatches são relógios inteligentes que possibilitam a interação do motorista com as notificações oriundas de um celular smartphone. O uso deste relógio durante a condução de automóvel pode provocar aumento na carga de trabalho mental do motorista, já que ambas as atividades disputam os mesmos recursos cognitivos e visuais. Uma forma promissora para aferir a carga de trabalho mental é através da dilatação do diâmetro pupila. Diferentes métodos foram desenvolvidos para aferir a carga de trabalho mental com base nos dados de diâmetro da pupila. Para investigar os efeitos da experiência do motorista e da demanda cognitiva resultante das tarefas de freada e do uso de smartwatch sobre a carga de trabalho mental, durante a condução de automóvel e avaliar como métodos de pupilometria (Index of Pupillary Activity, Low/Hight Index of Pupillary Activity e Task-evoked Pupillary Response, respectivamente, IPA, LHIPA e TEPR) são sensíveis às variações da carga de trabalho mental, durante a condução de automóvel, 40 voluntários, sendo 20 motoristas experientes e 20 novatos, foram selecionados para dirigir em quatro condições experimentais – controle (somente dirigir o automóvel), dirigir o automóvel com situação de freada, dirigir o automóvel com leitura do smartwatch e dirigir o automóvel com a situação de freada ocorrendo simultaneamente à leitura do smartwatch. Resultados mostraram que ao aumentar-se a demanda cognitiva nas condições experimentais, notou-se uma piora no desempenho de motoristas: diminuição da velocidade média, aumento da variabilidade da velocidade média, aumento da distância ao carro líder e aumento no número de colisões. Motoristas experientes apresentaram menor velocidade média e maior variabilidade da velocidade média. Os métodos IPA, LHIPA e TEPR foram sensíveis à manipulação da demanda cognitiva nas condições experimentais.
With the variety of electronic devices available in the internal environment of the vehicle, there has been increased concern about the possibility of these devices diverting the driver's attention from the task of driving a vehicle in traffic. Smartwatches are smart devices that allow drivers to interact with notifications from a smartphone. Using a smartwatch while driving can increase the driver’s mental workload, as both tasks compete for the same cognitive and visual resources. A promising way to measure mental workload is through pupil dilation. Different methods have been developed to measure mental workload based on pupil diameter data. To investigate the effects of driver’s experience and cognitive demands resulting from braking and smartwatch use tasks on mental workload while driving a car and to evaluate how pupillometry methods (Pupillary Activity Index, Low/High Index of Pupillary Activity, and Task-Evoked Pupillary Response, respectively, IPA, LHIPA, and TEPR) are sensitive to variations in mental workload while driving a car, 40 volunteers (20 experienced drivers and 20 novice drivers) were selected to drive in four experimental conditions - control (only driving the vehicle), driving with braking situation, driving with smartwatch reading and driving with the braking situation occurring simultaneously with the smartwatch reading. Results showed that when cognitive demands were increased in the experimental conditions, the driver’s performance was compromised (a decrease in the average speed, an increase in the variability of the average speed, an increase in the distance to the leading car, and an increase in the number of collisions). Experienced drivers had lower mean speed and higher mean speed variability. IPA, LHIPA, and TEPR methods were sensitive to the manipulation of cognitive demands in experimental conditions.
With the variety of electronic devices available in the internal environment of the vehicle, there has been increased concern about the possibility of these devices diverting the driver's attention from the task of driving a vehicle in traffic. Smartwatches are smart devices that allow drivers to interact with notifications from a smartphone. Using a smartwatch while driving can increase the driver’s mental workload, as both tasks compete for the same cognitive and visual resources. A promising way to measure mental workload is through pupil dilation. Different methods have been developed to measure mental workload based on pupil diameter data. To investigate the effects of driver’s experience and cognitive demands resulting from braking and smartwatch use tasks on mental workload while driving a car and to evaluate how pupillometry methods (Pupillary Activity Index, Low/High Index of Pupillary Activity, and Task-Evoked Pupillary Response, respectively, IPA, LHIPA, and TEPR) are sensitive to variations in mental workload while driving a car, 40 volunteers (20 experienced drivers and 20 novice drivers) were selected to drive in four experimental conditions - control (only driving the vehicle), driving with braking situation, driving with smartwatch reading and driving with the braking situation occurring simultaneously with the smartwatch reading. Results showed that when cognitive demands were increased in the experimental conditions, the driver’s performance was compromised (a decrease in the average speed, an increase in the variability of the average speed, an increase in the distance to the leading car, and an increase in the number of collisions). Experienced drivers had lower mean speed and higher mean speed variability. IPA, LHIPA, and TEPR methods were sensitive to the manipulation of cognitive demands in experimental conditions.
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Palavras-chave
Pupilometria, Carga de trabalho mental, Simulador de automóvel, Smartwatch, Movimento dos olhos, Freada, Pupillometry, Mental workload, Driving simulator, Eye movements, Braking