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Blood Alcohol Content

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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.