Philippe Simon

Using driver's head movements evolution as a drowsiness indicator

Many studies have been conducted to try to put forward measurable indicators that could help in the design of alerting devices used to reliably monitor severe driver drowsiness. However, most of these indicators are either too sensitive to environment modifications (performance indicators), invasive for the driver (EEG, EOG...) or are complex and expensive to measure (Eye blinks, PERCLOS...). This study aims at proposing a set of indicators related to driver performance and physiology that could be efficient for monitoring driver drowsiness and be easy to measure. Thirty two subjects took part to a long duration simulated highway trip (336 km) during which performance and physiological data were recorded The analysis of these data puts forward two sets of indicators : firstly, a set of performance indicators whose evolutions are consistent with those described in literature. They ensure the detection of drowsiness during the experiment. Secondly, a set of physiological indicators : the head movements dispersion. This has revealed itself to be a very good indicator as it evolves in the same way as the performance ones on long lasting periods while being poorly influenced by the road environment, contrary to most performance indicators.

Quantitative analysis of mental workload influence on eye scanning movements

The eye scanning behaviour is studied according to the momentary mental workload. Subjects perform a set of five concurrent sensorimotor tasks. The tasks demands switch between two levels-high and low-during the entire experiment. No significant changes are quantitatively found in indexes related to characteristics of ocular fixations and saccades between the two difficulty levels. On the other hand, results dealing with transitions between the five tasks and scanpaths agree with previous research indicating that visual behavior tends towards more organization-or less disorder-when difficulty increases.<<ETX>>

Fuzzy Segmentation for the Exploratory Analysis of Multidimensional Signals: Example From a Study on Driver Overtaking Behavior

This paper explains the key role played by windowing in the preliminary analysis of multifactor and multivariate (MFMV) databases. The explanation is based on the general case of a database featuring quantitative or qualitative measurement variables and a hyperparallelepipedic structure in which the directions correspond to the factors. In order to maintain the MFMV aspects of this data structure, the windowing approach as described in this paper does not reduce the information as much as most of the basic non-windowing summarizing procedures using the standard statistical indicators. First, the data in each cell of the hyperparallelepiped are transformed into membership values that can be averaged over factors, such as time or individuals. Then, these membership values may be potentially investigated into with several graphic techniques; for this paper, multiple correspondence analysis (MCA) was chosen. The presentation fall into two parts. First, a didactic example based on a simulated data set describes the approach in comparison with more traditional approaches, and then a real data set, with multidimensional signals recorded for 34 subjects in 15 experimental overtaking situations, is used to demonstrate the power of the “space windowing/MCA” pair on a large real database. Next, the discussion section weighs out the pros and the cons of using space windowing to perform a preliminary analysis of a large MFMV database in studies of human component systems.

From classic statistical characterization to fuzzy windowing based characterization for the exploratory analysis of miscellaneous time variables: example in the field of car driving studies

The problem of data characterization of quantitative and qualitative measurement scales is stated in the context of an exploratory multivariate statistical analysis. An example from a car driving study is considered where the quantitative data correspond to the car and head movements, while the qualitative data correspond to objects being viewed--road, bridge, sign-post, etc. For each of these two sets, the literature is analyzed first in terms of data characterizing methods and relationship obtaining methods. Then we propose to evaluate and compare nine quantitative data characteriing methods: five corresponding to classic statistical indicators, two to crisp space windowing with either two or three windows, and two on fuzzy windowing with either two or three windows. Logically the last method appears as the best (according to our evaluation procedure). Then we propose a bidimensional fuzzy windowing instead of a crisp one to characterize the gaze positions. Finally the multiple correspondence analysis is used to investigate the membership value averages obtained from the characterization stage.

Using space windowing for a preliminary analysis of complex time data in human component system studies. Examples with eye-tracking in advertising and car/head movements in driving

Empirical studies of human systems often involve recording multidimensional signals because the system components may require physical measurements (e.g., temperature, pressure, body movements and/or movements in the environment) and physiological measurements (e.g., electromyography or electrocardiography). Analysis of such data becomes complex if both the multifactor aspect and the multivariate aspect are retained. Three examples are used to illustrate the role of fuzzy space windowing and the large number of data analysis paths. The first example is a classic simulated data set found in the literature, which we use to compare several data analysis paths generated with principal component analysis and multiple correspondence analysis with crisp and fuzzy windowing. The second example involves eye-tracking data based on advertising, with a focus on the case of one category variable, but with the possibility of several space windowing models and time entities. The third example concerns car and head movement data from a driving vigilance study, with a focus on the case involving several quantitative variables. The notions of analysis path multiplicity and information are discussed both from a general perspective and in terms of our two real examples.

New flavin and deazaflavin oligonucleotide conjugates for the amperometric detection of DNA hybridization

The functionalization of an oligonucleotide by flavin and deazaflavin derivatives allowed an amperometric determination of the hybridization process through the disappearance of the electroactivity of the free oligonucleotide and the appearance of a new electrochemical signal characteristic of the resulting duplex.

Navigation Sensors Architecture for the Automated Transfer Vehicle

Abstract The Automated Transfer Vehicle (ATV) is a European Space Agency (ESA) funded unmanned spacecraft designed and developed by several European aerospace companies in the frame of the International Space Station (ISS). Its function is threefold: to supply the station with fret and propellant, to reboost ISS to a viable orbit and to retrieve waste from the station and bum them down during ATV re-entry in atmosphere. The ATV will be the first European space vehicle to perform an orbital rendezvous. To fulfill its mission, it relies on a complex avionics architecture (gyro assembly, star tracker, GPS, rendezvous sensors and accelerometers) that is designed to sustain stringent launch environment and to provide measurements in order to perform various missions such as ATV stabilization, collision avoidance maneuver (to ensure ISS safety), autonomous docking to ISS, dedocking and re-entry.

Using correspondence analysis with a large set of transition matrices. Example with eye movement data and fuzzy space windowing

This paper considers the data analysis process as a chronology of action triplets in which both the first and the third actions of each single triplet require a human intervention and the in-between action requires a computational approach. This process is used for the investigation of time units where each unit can be described using a qualitative variable with either crisp or fuzzy features. Then, each time, a unit set is characterized via a transitional matrix (TM). If in most cases only one TM is plotted with the primary objective of generating a Markov process model featuring S behavior states, this paper therefore considers the case where several TMs are used, e.g. several individuals yield several matrices in an experimental design. To investigate the transition data sets, we suggest using Correspondence Analysis (CA) via an input table containing the values from these TMs, each row including the S*S values of each TM. In addition, several new TMs can be computed from the initial TM, for instance one overall table for each individual or for each experimental condition. Within CA, these additional TMs can be considered supplementary row-points to demonstrate to what extent individual behaviors are similar. To illustrate this use of CA, we consider data from 1) simulated signals and 2) a driving vigilance study during which eye movements were recorded and characterized though eye fixation TM with S fuzzy areas of interest. The benefits and drawbacks of our approach are discussed and a comparison is performed with two hierarchical clustering processes used respectively with the transition matrices and modality pairs.

USING CORRESPONDENCE ANALYSIS WHEN THE SYSTEM DESCRIPTION YIELDS SEVERAL TRANSITION MATRICES. EXAMPLES WITH SIMULATED DATA AND REAL DRIVER-CAR-ENVIRONMENT SYSTEM DATA

This article considers systems described in terms of a qualitative variable with S states, investigated in numerous empirical situations. Thus, the experimental design or observational design yields N time data sets, one for each empirical situation. Each data set is characterized by an S*S transition matrix and all the transition values are placed on a data table with N rows and S*S columns. The table is subjected to correspondence analysis, which highlights the inter- and intra-individual differences. To illustrate the approach, a didactic example using simulated data is presented, followed by an actual example using eye movement in car driving.

Study of multidimensional signals from human control behavior experiments. Principle and example with Multiple Correspondence Analysis.

Abstract Human control experimental studies often yield many data sets which are large due to the presence of several factors and variables. This article proposes the study of a large database of multidimensional signals using two main tools: fuzzy windowing (FW) and multiple correspondence analysis (MCA). Such a pair of tools may help the data analyst to see the more salient phenomena (and data imperfections, if there are any). One example concerning an experimental study about lane change using a driving simulator is considered to illustrate this. This article shows how FW allows quantitative and qualitative scales to be studied together and how MCA may draw nonlinear relationships. The possibility of considering data sets as supplementary points may help the data analyst to understand the effects of the factors. The discussion states both the disadvantages and advantages of the proposed approach.