Relationship Between Kinematic and Physiological Indices During Braking Events of Different Intensities. (May 2018)

• Record Type: Journal Article
• Authors: Musicant, Oren; Botzer, Assaf; Laufer, Ilan; Collet, Christian
• Is Part Of: Human factors. Volume 60:Number 3(2018)
• Page Start: 415
• Page End: 427
• Publication Date: 2018-05
• Subjects: mental workload -- effort -- physiology -- motor control -- kinematics; Human engineering -- Periodicals; 620.82
• DOI: 10.1177/0018720817752595
• Languages: English
• ISSNs: 0018-7208

Abstract

Objective: To study the relationship between physiological indices and kinematic indices during braking events of different intensities. Background: Based on mental workload theory, driving and other task demands may generate changes in physiological indices, such as the driver's heart rate and skin conductance. However, no attempts were made to associate changes in physiological indices with changes in vehicle kinematics that result from the driver attempts to meet task demands. Method: Twenty-five drivers participated in a field experiment. We manipulated braking demands using roadside signs to communicate the speed (km/h) before braking (50 or 60) and the target speed for braking (30 or to a complete stop). In an additional session, we asked drivers to brake as if they were responding to an impending collision. We analyzed the relationship between the intensities of braking events as measured by deceleration values (g) and changes in heart rate, heart rate variability, and skin conductance. Results: All physiological indices were associated with deceleration intensity. Especially salient were the differences in physiological indices between the intensive (|g| > 0.5) and nonintensive braking events. The strongest relationship was between braking intensity and skin conductance. Conclusions: Skin conductance, heart rate, and heart rate variability can mirror the mental workload elicited by varying braking intensities. Application: Associating vehicle kinematics with physiological indices related to short-term driving events may help improve the performance of driver assistance systems.