Psychomotor Impairment Detection via Finger Interactions with a Computer Keyboard During Natural Typing

Modern digital devices and appliances are capable of monitoring the timing of button presses, or finger interactions in general, with a sub-millisecond accuracy. However, the massive amount of high resolution temporal information that these devices could collect is currently being discarded. Multiple studies have shown that the act of pressing a button triggers well defined brain areas which are known to be affected by motor-compromised conditions. In this study, we demonstrate that the daily interaction with a computer keyboard can be employed as means to observe and potentially quantify psychomotor impairment. We induced a psychomotor impairment via a sleep inertia paradigm in 14 healthy subjects, which is detected by our classifier with an Area Under the ROC Curve (AUC) of 0.93/0.91. The detection relies on novel features derived from key-hold times acquired on standard computer keyboards during an uncontrolled typing task. These features correlate with the progression to psychomotor impairment (p < 0.001) regardless of the content and language of the text typed, and perform consistently with different keyboards. The ability to acquire longitudinal measurements of subtle motor changes from a digital device without altering its functionality may allow for early screening and follow-up of motor-compromised neurodegenerative conditions, psychological disorders or intoxication at a negligible cost in the general population.Comunidad de Madri

AUTENTICAÇÃO BIOMÉTRICA PARA SISTEMAS POR MEIO DA DINÂMICA DA DIGITAÇÃO

Biometric authentication through typing dynamics offers security for more systems, coupled with the traditional login and password, as it associates authentication with biometric factors. This work presents methodologies for biometric authentication performance from the middle of the typing dynamics, techniques for extracting characteristics after capturing the time in which to press and release as keys. The methods used were: Mahalanobis distance classifier (classification and authentication) and Random Forest (classification).A autenticação biométrica por meio da dinâmica da digitação, oferece uma segurança a mais para os sistemas, se associando ao tradicional login e senha, pois associa autenticação com fatores biométricos. Esse trabalho apresenta metodologias para a realização da autenticação biométrica po meio da dinâmica da digitação, utilizando técnicas para a extração de características após capturar o tempo em que se pressionar e soltar as teclas. Os métodos utilizados foram: Classificador baseado na distância de Mahalanobis (classificação e autenticação) e Floresta Aleatória (classificação)

Psychomotor Impairment Detection via Finger Interactions with a Computer Keyboard During Natural Typing

Modern digital devices and appliances are capable of monitoring the timing of button presses, or finger interactions in general, with a sub-millisecond accuracy. However, the massive amount of high resolution temporal information that these devices could collect is currently being discarded. Multiple studies have shown that the act of pressing a button triggers well defined brain areas which are known to be affected by motor-compromised conditions. In this study, we demonstrate that the daily interaction with a computer keyboard can be employed as means to observe and potentially quantify psychomotor impairment. We induced a psychomotor impairment via a sleep inertia paradigm in 14 healthy subjects, which is detected by our classifier with an Area Under the ROC Curve (AUC) of 0.93/0.91. The detection relies on novel features derived from key-hold times acquired on standard computer keyboards during an uncontrolled typing task. These features correlate with the progression to psychomotor impairment (p < 0.001) regardless of the content and language of the text typed, and perform consistently with different keyboards. The ability to acquire longitudinal measurements of subtle motor changes from a digital device without altering its functionality may allow for early screening and follow-up of motor-compromised neurodegenerative conditions, psychological disorders or intoxication at a negligible cost in the general population

The design of an evolutionary algorithm for artificial immune system based failure detector generation and optimization

The development of an evolutionary algorithm and accompanying software for the generation and optimization of artificial immune system-based failure detectors is presented in this thesis. These detectors use the Artificial Immune System-based negative selection strategy. The utility is a part of an integrated set of methodologies for the detection, identification, and evaluation of a wide variety of aircraft sub-system abnormal conditions. The evolutionary algorithm and accompanying software discussed in this document is concerned with the creation, optimization, and testing of failure detectors based on the negative selection strategy. A preliminary phase consists of processing data from flight tests for self definition including normalization, duplicate removal, and clustering. A first phase of the evolutionary algorithm produces, through an iterative process, a set of detectors that do not overlap with the self and achieve a prescribed level of coverage of the non-self. A second phase consists of a classic evolutionary algorithm that attempts to optimize the number of detectors, overlapping between detectors, and coverage of the non-self while maintaining no overlapping with the self. For this second phase, the initial population is composed of sets of detectors, called individuals, obtained in the first phase. Specific genetic operators have been defined to accommodate different detector shapes, such as hyper-rectangles, hyper-spheres, hyper-ellipsoids and hyper-rotational-ellipsoids. The output of this evolutionary algorithm consists of an optimized set of detectors which is intended for later use as a part of a detection, identification, and evaluation scheme for aircraft sub-system failure.;An interactive design environment has been developed in MATLAB that relies on an advanced user-friendly graphical interface and on a substantial library of alternative algorithms to allow maximum flexibility and effectiveness in the design of detector sets for artificial immune system-based abnormal condition detection. This user interface is designed for use with Windows and MATLAB 7.6.0, although measures have been taken to maintain compatibility with MATLAB version 7.0.4 and higher, with limited interface compatibility. This interface may also be used with UNIX versions of MATLAB, version 7.0.4 or higher.;The results obtained show the feasibility of optimizing the various shapes in 2, 3, and 6 dimensions. Hyper-spheres are generally faster than the other three shapes, though they do not necessarily exhibit the best detection results. Hyper-ellipsoids and hyper-rotational-ellipsoids generally show somewhat better detection performance than hyper-spheres, but at a higher calculation cost. Calculation time for optimization of hyper-rectangles seems to be highly susceptible to dimensionality, taking increasingly long in higher dimensions. In addition, hyper-rectangles tend to need a higher number of detectors to achieve adequate coverage of the solution space, though they exhibit very little overlapping among detectors. However, hyper-rectangles are consistently and considerably quicker to calculate detection for than the other shapes, which may make them a promising candidate for online detection schemes

A new approach to securing passwords using a probabilistic neural network based on biometric keystroke dynamics

Passwords are a common means of identifying an individual user on a computer system. However, they are only as secure as the computer user is vigilant in keeping them confidential. This thesis presents new methods for the strengthening of password security by employing the biometric feature of keystroke dynamics. Keystroke dynamics refers to the unique rhythm generated when keys are pressed as a person types on a computer keyboard. The aim is to make the positive identification of a computer user more robust by analysing the way in which a password is typed and not just the content of what is typed. Two new methods for implementing a keystroke dynamic system utilising neural networks are presented. The probabilistic neural network is shown to perform well and be more suited to the application than traditional backpropagation method. An improvement of 6% in the false acceptance and false rejection errors is observed along with a significant decrease in training time. A novel time sequenced method using a cascade forward neural network is demonstrated. This is a totally new approach to the subject of keystroke dynamics and is shown to be a very promising method The problems encountered in the acquisition of keystroke dynamics which, are often ignored in other research in this area, are explored, including timing considerations and keyboard handling. The features inherent in keystroke data are explored and a statistical technique for dealing with the problem of outlier datum is implemented.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Identifying users using Keystroke Dynamics and contextual information

Biometric identification systems based on Keystroke Dynamics have been around for almost forty years now. There has always been a lot of interest in identifying individuals using their physiological or behavioral traits. Keystroke Dynamics focuses on the particular way a person types on a keyboard. The objective of the proposed research is to determine how well the identity of users can be established when using this biometric trait and when contextual information is also taken into account. The proposed research focuses on free text. Users were never told what to type, how or when. This particular field of Keystroke Dynamics has not been as thoroughly studied as the fixed text alternative where a plethora of methods have been tried. The proposed methods focus on the hypothesis that the position of a particular letter, or combination of letters, in a word is of high importance. Other studies have not taken into account if these letter combinations had occurred at the beginning, the middle, or the end of a word. A template of the user will be built using the context of the written words and the latency between successive keystrokes. Other features, like word length, minimum number of needed words to consider a session valid, frequency of words, model building parameters, as well as age group and gender have also been studied to determine those that better help ascertain the identity of an individual. The results of the proposed research should help determine if using Keystroke Dynamics and the proposed methodology are enough to identify users from the content they type with a good enough level of certainty. From this moment, it could be used as a method to ensure that a user is not supplanted, in authentication schemes, or even to help determine the authorship of different parts of a document written by more than one user.Els sistemes d’identificació biomètrica basades en la cadència de tecleig fa gairebé quaranta anys que s’estudien. Hi ha hagut molt interès en identificar les persones a partir de les seves característiques fisiològiques o de comportament. La cadència de tecleig és la manera en la que una persona escriu en un teclat. L’objectiu de la recerca proposada és determinar com de bé es pot arribar a identificar un individu mitjançant aquesta característica biomètrica i quan també es prenen en consideració dades contextuals. Aquesta recerca es basa en text lliure. Als usuaris mai se’ls va dir què, quan o com havien d’escriure. Aquest camp de la cadència de tecleig no ha estat tan estudiat com l’alternativa de text fix on un gran ventall de mètodes s’han provat. Els mètodes d’identificació proposats es basen en la hipòtesi que la posició d’una lletra, o combinació de lletres teclejades, en una paraula és de gran importància. Altres estudis no prenen en consideració aquesta informació, és a dir, si la combinació de lletres s’ha produït al principi, al mig o al final de la paraula. Es crearà una empremta de l’usuari tenint en compte el context de les lletres en les paraules escrites i les latències entre pulsacions successives. Altres característiques com la mida de les paraules, el nombre mínim de paraules necessari per considerar una sessió vàlida, la freqüència de mots, els paràmetres de construcció dels models, així com el grup d’edat i el gènere també s’han estudiat per determinar quines són les que millor ajuden a identificar un individu. Els resultats de la recerca proposada haurien de permetre determinar si l’ús de la cadència de tecleig i els mètodes proposats són suficients per identificar els usuaris a partir del contingut que generen, sempre amb un cert marge d’error. En cas afirmatiu es podria introduir la tècnica proposada com un mètode més per assegurar que un usuari no és suplantat, en sistemes d’autenticació, o fins i tot per ajudar a determinar l’autoria de diferents parts d’un document que ha estat escrit per més d’un usuari