Stéphanie Dabic

A fuzzy model relating vibrotactile signal characteristics to haptic sensory evaluations

Nowadays tactile surfaces are slowly replacing the mechanical interfaces of our electronic devices, and the actual trend is toward a quasi-total touch interaction. This transition has however one important side effect, i.e the lack of feedback from the device, which in certain situations can be crucial. In order to overcome this, it has been suggested that feedback has to be delivered to the finger through vibrations that should be both detectable and comfortable. This paper aims to define a perception model for the sensory evaluations of the vibrotactile signals using fuzzy set theory. First of all, the hypothesis that haptic perception is strongly related to physical characteristics of the signals was evaluated and confirmed with a 93% correlation rate, based on psychophysical studies of the tactile sense. Secondly, using the previous analysis as a knowledge base we have implemented a fuzzy inference system which forecasts the preference values for vibrotactile signals. The preliminary results show that for 15 out of 18 signals, the preference is correctly predicted within a reasonable uncertainty interval.

A Fuzzy Rule-Based Model of Vibrotactile Perception via an Automobile Haptic Screen

With the increased popularity of touch-sensitive surfaces, much attention has been drawn to their security-related issues, as they currently rely only on the visual sense for feedback. To improve operability, vibrotactile signals may be delivered to the finger on screen interaction. The way vibrotactile signals affect human perception is examined via three measured variables, related to their energy, velocity, and spectral complexity, and which are analytically defined in this paper. It is shown that these variables accurately account for the psychophysical properties of the tactile sense. Based on this, a psychophysical fuzzy rule-based model of vibrotactile perception is introduced to forecast the comfort values of the vibrational signals provided by an automobile haptic screen. Using an efficient rule-based generation method, a Mamdani fuzzy inference system is proposed; it achieves a mean error rate of 14% for the train set and 17% for the test set, while correctly classifying most of the signals within a reasonable tolerance, related to human evaluation imprecision. The system also produces a comprehensible linguistic rule structure, which allows behavioral patterns to be detected.

Haptic modality takes its time: Dynamic of activations of sensory modalities in perceptual and memory processes.

Based on claims resulting from grounded cognition theory that perceptual and memory processes are using the same distributed systems, the present study investigated the temporal aspect of access to memory traces through haptic and auditory modalities. Unlike in the case of visual or auditory components, the perception of a vibrotactile component is more sequential in nature and therefore cannot be fully processed before the end of the signal. The present study explores the dynamic of components activation in a situation of audio-vibrotactile asynchrony. We used a short-term priming paradigm consisting of an association phase (between a vibration and sound) and a test phase testing priming effect of a vibrotactile stimulation on the processing of a target sound. Results showed an interference with a simultaneous processing and a facilitation with a sequential processing. The temporality process of perceptual components is also important at a memory level.

A redundancy measure for efficient fuzzy rule-base reduction

Driven by the growing complexity of real-world systems, current trends in fuzzy system modeling employ ways to automatically learn the system rule-base from numerical data. While these approaches greatly improve model accuracy, the resulting rule-base is generally less interpretable than expert-driven rule-bases. We provide qualitative justification for this behavior and show that automatic rule-base generation leads to the occurrence of redundant rules, i.e. rules encoding approximately the same knowledge. In order to improve interpretability, redundant rules must be properly detected and removed. Therefore, this paper introduces a novel measure to estimate the redundancy of fuzzy rules based on the common influence exerted by a pair of rules over the data, and weighted by some distance measure between rules. The concept of common influence, defined therein, indicates how two rules are linked by the data distribution. Our approach is validated on some analytical function modeling task and then tested on a real-world problem dealing with vibrotactile stimuli characterization. Both experiments showed that removing the most redundant rules, according to the proposed redundancy measure, yields smaller rule-bases of up to 25%, with only negligible drops in accuracy.

A Fuzzy Rule-Based Haptic Perception Model for Automotive Vibrotactile Display

Currently, tactile surfaces implemented in automobiles are passive, i.e., feedbackless, thus forcing the user to visually check the device. To improve drivers’ interaction, surface vibrations can be used to deliver feedback to the finger when touched, and an associated perception model is required. Hence, this paper introduces a fuzzy model for the comfort degree of vibrotactile signals. System input variables are chosen from the physical characteristics of the signals, and are validated on a dissimilarity judgment task. The system achieves an error of 9% and correctly classifies 17 out of 18 signals within a reasonable interval. A graphical user interface to interact with the system is also presented.