The Experts below are selected from a list of 225 Experts worldwide ranked by ideXlab platform

Didier Puzenat - One of the best experts on this subject based on the ideXlab platform.

  • multi user Blood Alcohol Content estimation in a realistic simulator using artificial neural networks and support vector machines
    The European Symposium on Artificial Neural Networks, 2013
    Co-Authors: Audrey Robinel, Didier Puzenat
    Abstract:

    We instrumented a realistic car simulator to extract low level data re- lated to the driver's use of the vehicle controls. After proceeding these data, we generated features that were fed to a Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM). Our goal was determine if the driver's Blood Alcohol Content (BAC) was over 0.4g.l −1 or not, and even estimate the BAC value. Our de- vice process the vehicle's controls data and then outputs the user BAC. We discuss the results of the prototype using the MLP and SVM algorithms in both single-user and multi-user context for detection of drunk drivers and estimation of the BAC value. The prototype performed better with single user base than with multi-user, and provided comparable results with MLP and SVM. This paper corrects a small error in our previous publication in ESANN'12 (3).

  • ESANN - Multi-User Blood Alcohol Content Estimation in a Realistic Simulator using Artificial Neural Networks and Support Vector Machines
    2013
    Co-Authors: Audrey Robinel, Didier Puzenat
    Abstract:

    We instrumented a realistic car simulator to extract low level data re- lated to the driver's use of the vehicle controls. After proceeding these data, we generated features that were fed to a Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM). Our goal was determine if the driver's Blood Alcohol Content (BAC) was over 0.4g.l −1 or not, and even estimate the BAC value. Our de- vice process the vehicle's controls data and then outputs the user BAC. We discuss the results of the prototype using the MLP and SVM algorithms in both single-user and multi-user context for detection of drunk drivers and estimation of the BAC value. The prototype performed better with single user base than with multi-user, and provided comparable results with MLP and SVM. This paper corrects a small error in our previous publication in ESANN'12 (3).

  • Real Time Drunkness Analysis Through Games Using Artificial Neural Networks
    2011
    Co-Authors: Audrey Robinel, Didier Puzenat
    Abstract:

    In this paper, we describe a Blood Alcohol Content estimation prototype based on a comportment analysis performed by artificial neural networks. We asked to subjects that had drunk Alcohol to play a video-game after having measured their Blood Alcohol Content with a breathalyser. A racing game was modified so that it could provide various data related to the use of the controls by the player. Using the collected data, we trained our neural network in order to be able to determine whether or not the subject had exceeded a Blood Alcohol Content threshold. We also succeeded in estimating this Blood Alcohol Content with a mean error of 0.1g/l. We could perform those estimations independently of the track played among the two ones used. It was also performed in "real time", e.g., using only the data collected within the last 10 seconds of playing.

Audrey Robinel - One of the best experts on this subject based on the ideXlab platform.

  • multi user Blood Alcohol Content estimation in a realistic simulator using artificial neural networks and support vector machines
    The European Symposium on Artificial Neural Networks, 2013
    Co-Authors: Audrey Robinel, Didier Puzenat
    Abstract:

    We instrumented a realistic car simulator to extract low level data re- lated to the driver's use of the vehicle controls. After proceeding these data, we generated features that were fed to a Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM). Our goal was determine if the driver's Blood Alcohol Content (BAC) was over 0.4g.l −1 or not, and even estimate the BAC value. Our de- vice process the vehicle's controls data and then outputs the user BAC. We discuss the results of the prototype using the MLP and SVM algorithms in both single-user and multi-user context for detection of drunk drivers and estimation of the BAC value. The prototype performed better with single user base than with multi-user, and provided comparable results with MLP and SVM. This paper corrects a small error in our previous publication in ESANN'12 (3).

  • ESANN - Multi-User Blood Alcohol Content Estimation in a Realistic Simulator using Artificial Neural Networks and Support Vector Machines
    2013
    Co-Authors: Audrey Robinel, Didier Puzenat
    Abstract:

    We instrumented a realistic car simulator to extract low level data re- lated to the driver's use of the vehicle controls. After proceeding these data, we generated features that were fed to a Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM). Our goal was determine if the driver's Blood Alcohol Content (BAC) was over 0.4g.l −1 or not, and even estimate the BAC value. Our de- vice process the vehicle's controls data and then outputs the user BAC. We discuss the results of the prototype using the MLP and SVM algorithms in both single-user and multi-user context for detection of drunk drivers and estimation of the BAC value. The prototype performed better with single user base than with multi-user, and provided comparable results with MLP and SVM. This paper corrects a small error in our previous publication in ESANN'12 (3).

  • Real Time Drunkness Analysis Through Games Using Artificial Neural Networks
    2011
    Co-Authors: Audrey Robinel, Didier Puzenat
    Abstract:

    In this paper, we describe a Blood Alcohol Content estimation prototype based on a comportment analysis performed by artificial neural networks. We asked to subjects that had drunk Alcohol to play a video-game after having measured their Blood Alcohol Content with a breathalyser. A racing game was modified so that it could provide various data related to the use of the controls by the player. Using the collected data, we trained our neural network in order to be able to determine whether or not the subject had exceeded a Blood Alcohol Content threshold. We also succeeded in estimating this Blood Alcohol Content with a mean error of 0.1g/l. We could perform those estimations independently of the track played among the two ones used. It was also performed in "real time", e.g., using only the data collected within the last 10 seconds of playing.

Michelle L Fast - One of the best experts on this subject based on the ideXlab platform.

  • ICSH - Real-Time Prediction of Blood Alcohol Content Using Smartwatch Sensor Data
    Smart Health, 2016
    Co-Authors: Mario Gutierrez, Michelle L Fast
    Abstract:

    This paper proposes an application that collects sensor data from a smartwatch in order to predict drunkenness in real-time, discreetly, and non-invasively via a machine learning approach. This system could prevent drunk driving or other dangers related to the consumption of Alcohol by giving users a way to determine personal intoxication level without the use of intrusive breathalyzers or guess work. Using smartwatch data collected from several volunteers, we trained a machine learning model that may work with a smartphone application to predict the user's intoxication level in real-time.

  • real time prediction of Blood Alcohol Content using smartwatch sensor data
    ICSH 2015 Revised Selected Papers of the International Conference on Smart Health - Volume 9545, 2015
    Co-Authors: Mario Gutierrez, Michelle L Fast
    Abstract:

    This paper proposes an application that collects sensor data from a smartwatch in order to predict drunkenness in real-time, discreetly, and non-invasively via a machine learning approach. This system could prevent drunk driving or other dangers related to the consumption of Alcohol by giving users a way to determine personal intoxication level without the use of intrusive breathalyzers or guess work. Using smartwatch data collected from several volunteers, we trained a machine learning model that may work with a smartphone application to predict the user's intoxication level in real-time.

Rossana Moroni - One of the best experts on this subject based on the ideXlab platform.

  • statistical modelling of measurement errors in gas chromatographic analyses of Blood Alcohol Content
    Forensic Science International, 2010
    Co-Authors: Rossana Moroni, Paul Blomstedt, Lars Wilhelm, Tapani Reinikainen, Erkki Sippola, Jukka Corander
    Abstract:

    Headspace gas chromatographic measurements of ethanol Content in Blood specimens from suspect drunk drivers are routinely carried out in forensic laboratories. In the widely established standard statistical framework, measurement errors in such data are represented by Gaussian distributions for the population of Blood specimens at any given level of ethanol Content. It is known that the variance of measurement errors increases as a function of the level of ethanol Content and the standard statistical approach addresses this issue by replacing the unknown population variances by estimates derived from large sample using a linear regression model. Appropriate statistical analysis of the systematic and random components in the measurement errors is necessary in order to guarantee legally sound security corrections reported to the police authority. Here we address this issue by developing a novel statistical approach that takes into account any potential non-linearity in the relationship between the level of ethanol Content and the variability of measurement errors. Our method is based on standard non-parametric kernel techniques for density estimation using a large database of laboratory measurements for Blood specimens. Furthermore, we address also the issue of systematic errors in the measurement process by a statistical model that incorporates the sign of the error term in the security correction calculations. Analysis of a set of certified reference materials (CRMs) Blood samples demonstrates the importance of explicitly handling the direction of the systematic errors in establishing the statistical uncertainty about the true level of ethanol Content. Use of our statistical framework to aid quality control in the laboratory is also discussed.

Mario Gutierrez - One of the best experts on this subject based on the ideXlab platform.

  • ICSH - Real-Time Prediction of Blood Alcohol Content Using Smartwatch Sensor Data
    Smart Health, 2016
    Co-Authors: Mario Gutierrez, Michelle L Fast
    Abstract:

    This paper proposes an application that collects sensor data from a smartwatch in order to predict drunkenness in real-time, discreetly, and non-invasively via a machine learning approach. This system could prevent drunk driving or other dangers related to the consumption of Alcohol by giving users a way to determine personal intoxication level without the use of intrusive breathalyzers or guess work. Using smartwatch data collected from several volunteers, we trained a machine learning model that may work with a smartphone application to predict the user's intoxication level in real-time.

  • real time prediction of Blood Alcohol Content using smartwatch sensor data
    ICSH 2015 Revised Selected Papers of the International Conference on Smart Health - Volume 9545, 2015
    Co-Authors: Mario Gutierrez, Michelle L Fast
    Abstract:

    This paper proposes an application that collects sensor data from a smartwatch in order to predict drunkenness in real-time, discreetly, and non-invasively via a machine learning approach. This system could prevent drunk driving or other dangers related to the consumption of Alcohol by giving users a way to determine personal intoxication level without the use of intrusive breathalyzers or guess work. Using smartwatch data collected from several volunteers, we trained a machine learning model that may work with a smartphone application to predict the user's intoxication level in real-time.