Not just detecting but also predicting impairment of a car driver's operational state is a challenge. This study aims to determine whether the standard sources of information used to detect drowsiness can also be used to predict when a given drowsiness level will be reached. Moreover, we explore whether adding data such as driving time and participant information improves the accuracy of detection and prediction of drowsiness. Twenty-one participants drove a car simulator for 110min under conditions optimized to induce drowsiness. We measured physiological and behavioral indicators such as heart rate and variability, respiration rate, head and eyelid movements (blink duration, frequency and PERCLOS) and recorded driving behavior such as time-to-lane-crossing, speed, steering wheel angle, position on the lane. Different combinations of this information were tested against the real state of the driver, namely the ground truth, as defined from video recordings via the Trained Observer Rating. Two models using artificial neural networks were developed, one to detect the degree of drowsiness every minute, and the other to predict every minute the time required to reach a particular drowsiness level (moderately drowsy). The best performance in both detection and prediction is obtained with behavioral indicators and additional information. The model can detect the drowsiness level with a mean square error of 0.22 and can predict when a given drowsiness level will be reached with a mean square error of 4.18min. This study shows that, on a controlled and very monotonous environment conducive to drowsiness in a driving simulator, the dynamics of driver impairment can be predicted.
Previous studies have shown that consumers with higher affect intensity expressed stronger preferences for softer car seat fabrics (Kergoat et al.). The present research aims to consolidate and expand these results. Across two studies, we attempt to determine whether the intensity of affect (as measured by the affect intensity measure; Larsen) is a more general construct involved in soft textile preferences. Through the evaluation of two product categories (car seat fabrics and washed‐shirt fabrics) and the manipulation of product sensory attributes, we were able to establish that affect intensity components (positive intensity and negative reactivity) play a role in soft textile preferences, independent of the product category. The highest predictive value of particular affect intensity components for softness preference is discussed in line with the multidimensional approach of the affect intensity construct (Bryant et al.). PRACTICAL APPLICATIONS This research highlights the significance of one emotional individual difference dimension (affect intensity) accounting for consumer tactile sensory preferences. Practically, it offers a way to characterize clusters of heterogeneous tactile sensory preferences observed in consumer tests. Furthermore, it represents a step in the understanding of underlying processes involved in soft tactile sensory preferences. We can assume these implications are not limited to the sense of touch and/or non‐food products. As a general emotional variable, the affect intensity construct must play a role in various blind sensory evaluation settings and be a significant tool for a typology of consumers.
10th International Conference on Haptics - Perception, Devices, Control, and Applications (EuroHaptics), Imperial Coll London, London, ENGLAND, JUL 04-07, 2016International audienceCurrent touchscreen technology makes for intuitive human-computer interactions but often lacks haptic feedback offered by conventional input methods. Typing text on a virtual keyboard is arguably the task in which the absence of tactile cues imparts performance and comfort the most. Here we investigated the feasibility of modulating friction via ultrasonic vibration as a function of the pressing force to simulate a tactile feedback similar to a keystroke. Ultrasonic vibration is generally used to modulate the sliding friction which occurs when a finger moves laterally on a surface. We found that this method is also effective when the exploratory motion is normal to the surface. Psychophysical experiments show that a mechanical detent is unambiguously perceived in the case of signals starting with a high level of friction and ending to a low friction level. A weaker effect is experienced when friction is increasing with the pressure exerted by the finger, which suggests that the mechanism involved is a release of the skin stretch accumulated during the high-friction state
International audienceUltrasonic friction reduction is one potential technology for bringing tangibility to flat touchscreens. We previously established that this approach can be used to create an artificial sensation of pressing a mechanical switch by varying the coefficient of friction, which depends on the force applied by the user. This sensation proves effective majority of, but a non-negligible fraction reported feeling only weak sensations or none at all. In the present study, we examined the factors possibly involved in producing a vivid perception of a stimulus by measuring the mechanical impedance of the fingertip as an index to the frictional behavior, and performing psychophysical experiments. Subjects who experienced weaker sensations were found to have a weaker susceptibility to friction modulation, which may in turn be attributable to either a larger or a smaller than average/normal impedance; whereas those with a mechanical impedance of around 55 N.s/m clearly perceived the ultrasonic switch. Measuring and factoring the users impedance in real time could therefore provide a useful means of improving the rendering of ultrasonic surface haptic devices
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