This preliminary study focuses on the goal of developing and testing a setup and method for non-invasive monitoring of individuals using biosensors in a professional driving simulator (VI-grade Compact Simulator). This involves the synchronization and integration of hardware and software components. To detect the emotional and cognitive state of the driver, it is crucial to identify which signals provide reliable information about their condition. The objective of this study is to observe individuals in a controlled and repeatable environment designed to stimulate cognitive workload. This was achieved using a multimodal assessment method (iMotions), which includes eye tracking, galvanic skin response (GSR), electromyography (EMG), and respiration measurements, all conducted during two distinct controlled driving simulation scenarios. Four healthy subjects (average age = 24, standard deviation = ±2) were monitored during the first scenario, a highway with repeated emergency maneuvers (slalom through cones and double lane change), and the second, five laps of the Paul Ricard circuit. All of this for a total duration of approximately 20 minutes. The participants were not aware that the scenarios were designed to provoke different reactions. This experimental thesis aims to be the continuation and evolution of a testing phase previously conducted during an internship at iMotions, a company that develops multimodal streaming software and distributes commercial hardware. The hardware was supplied to the NAVLAB at the University of Padua, where the simulator is located. The results obtained, at first analysis, appear to be consistent with the literature, suggesting that a multimodal approach to physiological signals may characterize emotional and cognitive states in driving scenarios.
This preliminary study focuses on the goal of developing and testing a setup and method for non-invasive monitoring of individuals using biosensors in a professional driving simulator (VI-grade Compact Simulator). This involves the synchronization and integration of hardware and software components. To detect the emotional and cognitive state of the driver, it is crucial to identify which signals provide reliable information about their condition. The objective of this study is to observe individuals in a controlled and repeatable environment designed to stimulate cognitive workload. This was achieved using a multimodal assessment method (iMotions), which includes eye tracking, galvanic skin response (GSR), electromyography (EMG), and respiration measurements, all conducted during two distinct controlled driving simulation scenarios. Four healthy subjects (average age = 24, standard deviation = ±2) were monitored during the first scenario, a highway with repeated emergency maneuvers (slalom through cones and double lane change), and the second, five laps of the Paul Ricard circuit. All of this for a total duration of approximately 20 minutes. The participants were not aware that the scenarios were designed to provoke different reactions. This experimental thesis aims to be the continuation and evolution of a testing phase previously conducted during an internship at iMotions, a company that develops multimodal streaming software and distributes commercial hardware. The hardware was supplied to the NAVLAB at the University of Padua, where the simulator is located. The results obtained, at first analysis, appear to be consistent with the literature, suggesting that a multimodal approach to physiological signals may characterize emotional and cognitive states in driving scenarios.
Preliminary study for the measurement of Biosignals in Driving Simulators
MINEN, MICHELA
2022/2023
Abstract
This preliminary study focuses on the goal of developing and testing a setup and method for non-invasive monitoring of individuals using biosensors in a professional driving simulator (VI-grade Compact Simulator). This involves the synchronization and integration of hardware and software components. To detect the emotional and cognitive state of the driver, it is crucial to identify which signals provide reliable information about their condition. The objective of this study is to observe individuals in a controlled and repeatable environment designed to stimulate cognitive workload. This was achieved using a multimodal assessment method (iMotions), which includes eye tracking, galvanic skin response (GSR), electromyography (EMG), and respiration measurements, all conducted during two distinct controlled driving simulation scenarios. Four healthy subjects (average age = 24, standard deviation = ±2) were monitored during the first scenario, a highway with repeated emergency maneuvers (slalom through cones and double lane change), and the second, five laps of the Paul Ricard circuit. All of this for a total duration of approximately 20 minutes. The participants were not aware that the scenarios were designed to provoke different reactions. This experimental thesis aims to be the continuation and evolution of a testing phase previously conducted during an internship at iMotions, a company that develops multimodal streaming software and distributes commercial hardware. The hardware was supplied to the NAVLAB at the University of Padua, where the simulator is located. The results obtained, at first analysis, appear to be consistent with the literature, suggesting that a multimodal approach to physiological signals may characterize emotional and cognitive states in driving scenarios.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/52394