Mapping Grip Force Characteristics in the Measurement of Stress in Driving - PubMed (original) (raw)

Mapping Grip Force Characteristics in the Measurement of Stress in Driving

Yotam Sahar et al. Int J Environ Res Public Health. 2023.

Abstract

Reducing drivers' stress can potentially increase road safety. However, state-of-the-art physiological stress indices are intrusive and limited by long time lags. Grip force is an innovative index of stress that is transparent to the user and, according to our previous findings, requires a two- to five-second time window. The aim of this study was to map the various parameters affecting the relationship between grip force and stress during driving tasks. Two stressors were used: the driving mode and the distance from the vehicle to a crossing pedestrian. Thirty-nine participants performed a driving task during either remote driving or simulated driving. A pedestrian dummy crossed the road without warning at two distances. The grip force on the steering wheel and the skin conductance response were both measured. Various model parameters were explored, including time window parameters, calculation types, and steering wheel surfaces for the grip force measurements. The significant and most powerful models were identified. These findings may aid in the development of car safety systems that incorporate continuous measurements of stress.

Keywords: driving simulation; grip force; pedestrian; remote driving; steering wheel; stress.

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Conflict of interest statement

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1

Figure 1

Illustration of the operator station used to operate both the remotely controlled vehicle and the driving simulation (with the same HMI) while grip force on the steering wheel and electrodermal activity are measured.

Figure 2

Figure 2

Ariel University’s Mobile-Lab, used in the current research as the remotely controlled vehicle, is equipped with a three-camera set transmitting the live video to the remote driving station, as well as a remote driving system.

Figure 3

Figure 3

The steering wheel was embedded with 60 FSR sensors, which were grouped on three surfaces: close to the driver (grey circles in the illustration), on the circumference (black circles in the illustration), and on the surface far from the driver (white circles in the illustration).

Figure 4

Figure 4

Pedestrian dummy crossing manipulation, consisting of a pedestrian dummy (fixed on a skateboard moved by cables) being moved into the driving route at one of two distances in front of the vehicle: 4 m (close) or 8 m (far).

Figure 5

Figure 5

Driving route (for both remote driving and simulated driving conditions), consisting of road cones marking a slalom route with stationary pedestrian dummies as distractions, a U-turn marking and a crossing pedestrian dummy stress manipulation.

Figure 6

Figure 6

Grip force data collection and calculations. As illustrated, for each participant and each pedestrian crossing event, grip force data were calculated (Calca: maximum, mean, or standard deviation) for all sensors or for each surface at each sampling instance. A second calculation was applied to all samples in the time window (Calcb: maximum, mean, standard deviation, or median).

Figure 7

Figure 7

Raw grip force data from two crossing events of a single participant (randomly selected). _X_-axis represents time in relation to the crossing event (minus = before the event). _Y_-axis represents the maximal grip force at each measurement instance for all 60 sensors. The continuous line represents a ‘Close’ crossing event, while the dashed line represents a ‘Far’ crossing event.

Figure 8

Figure 8

Significance level (p) of LMM analyses of grip force as a function of the crossing distance with the participant as the random effect, various time window widths, and no offset from the crossing event. _X_-axis expresses the width (0.2 to 5 s, 0.2 s increments), and _Y_-axis expresses the significance level (p), logarithmically scaled for representation. The shape type represents the window calculation (circle = maximum, square = mean, triangle = SD, rhombus = median), and the fill color represents the sampling calculation (black = maximum, grey = mean, white = SD).

Figure 9

Figure 9

Grip force (window calculation: mean, single measurement event calculation for all 60 sensors; maximum in Newtons) as a function of the crossing distance. Boxes represent the inter-quartile range (IQR = Q1 to Q3) of the group, middle horizontal line represents the group’s median, upper line represents the largest value that is less than the upper quartile plus 1.5 times IQR, and lower line represents the smallest value that is greater than the lower quartile minus 1.5 times IQR. Asterisk (*) denotes a level of significance of p < 0.05.

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This study was supported by the Israeli Innovation Authority through the Andromeda consortium and by the Israeli National Road Safety Authority.

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