Muscle computer interfaces for driver distraction reduction

• Published in: Computer methods and programs in biomedicine Vol. 110; no. 2; pp. 137 - 149
• Main Authors: Khushaba, Rami N., Kodagoda, Sarath, Liu, Diaki, Dissanayake, Gamini
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ireland Ltd 01.05.2013; Elsevier
• Subjects: Accidents, Traffic - prevention & control; Adult; Algorithms; Applied sciences; Automobile Driving; Automobiles; Bayes Theorem; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data processing. List processing. Character string processing; Discriminant Analysis; Driver distraction; Electrodes; Electromyogram (EMG); Electromyography - methods; Exact sciences and technology; Female; Fingers - physiology; Fuzzy Logic; Ground, air and sea transportation, marine construction; Hand - physiology; Humans; Internal Medicine; Male; Memory organisation. Data processing; Muscles - physiology; Other; Pattern Recognition, Automated; Regression Analysis; Reproducibility of Results; Road transportation and traffic; Signal processing; Signal Processing, Computer-Assisted; Software; Young Adult; Electromyogram (EMG); Signal processing; Driver distraction; Wheel; Singularity; Crosscountry vehicle; Motor car; Matrix factorization; Motor vehicle; Network interfaces; Modeling; Posture; Vehicle driver; User interface; Selection criterion; Electromyography; Muscle; Pattern extraction; Electronic control; Discriminant analysis; Electronic equipment; Attention; Measurement sensor; Finger; Traffic accident; Pattern recognition; Experimental study; Real time; Fuzzy logic; Selection problem; QR factorization; Distraction
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2012.11.002

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 <7% error on average, proving the significance of the proposed method.