Emmanuelle Diaz

Ultrasonic Friction Modulation While Pressing Induces a Tactile Feedback

Current 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.

Optimal skin impedance promotes perception of ultrasonic switches

Ultrasonic 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.

Detection and prediction of driver drowsiness using artificial neural network models

Not just detecting but also predicting impairment of a car drivers 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.

Comparison of three hypothesis testing approaches for the selection of the appropriate number of clusters of variables

Within the scope of cluster analysis of variables, the selection of the appropriate number of clusters is of paramount interest. The strategy of determination of the appropriate number of clusters adopted herein is based on a hypothesis testing approach. It consists in testing whether the variation of a partition quality criterion between two consecutive partitions is far removed from the expected variation under the null-hypothesis stipulating a lack of structure. Three hypothesis testing strategies are detailed and compared in the scope of clustering of variables: Gap, Weighted Gap and a statistic associated with CLV methodology. Finally, an illustration is presented based on data from a preference study.