Muscle computer interfaces for driver distraction reduction

  • Authors:

  • Rami N. Khushaba;Sarath Kodagoda;Diaki Liu;Gamini Dissanayake

  • Affiliations:

  • School of Electrical, Mechanical, and Mechatronic Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Australia;School of Electrical, Mechanical, and Mechatronic Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Australia;School of Electrical, Mechanical, and Mechatronic Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Australia;School of Electrical, Mechanical, and Mechatronic Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Australia

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2013

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Abstract

Driver distraction is regarded as a significant contributor to motor-vehicle crashes. One of the important factors contributing to driver distraction was reported to be the handling and reaching of in-car electronic equipment and controls that usually requires taking the drivers' hands off the wheel and eyes off the road. To minimize the amount of such distraction, we present a new control scheme that senses and decodes the human muscles signals, denoted as Electromyogram (EMG), associated with different fingers postures/pressures, and map that to different commands to control external equipment, without taking hands off the wheel. To facilitate such a scheme, the most significant step is the extraction of a set of highly discriminative feature set that can well separate between the different EMG-based actions and to do so in a computationally efficient manner. In this paper, an accurate and efficient method based on Fuzzy Neighborhood Discriminant Analysis (FNDA), is proposed for discriminant feature extraction and then extended to the channel selection problem. Unlike existing methods, the objective of the proposed FNDA is to preserve the local geometrical and discriminant structures, while taking into account the contribution of the samples to the different classes. The method also aims to efficiently overcome the singularity problems of classical LDA by employing the QR-decomposition. Practical real-time experiments with eight EMG sensors attached on the human forearm of eight subjects indicated that up to fourteen classes of fingers postures/pressures can be classified with