Jiří Mekyska

An Information Analysis of In-Air and On-Surface Trajectories in Online Handwriting

This paper is aimed at analysing, from an information theory perspective, the gestures produced by human beings when handwriting a text. Modern capturing devices allow the gathering of data not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. Our past research with isolated uppercase words clearly suggests that both types of trajectories have a biometric potential to perform writer recognition and that they can be effectively combined to enhance the recognition accuracy. With samples from the BiosecurID database, we have analysed the entropy of each kind of trajectories, as well as the amount of information they share, and the difference between intra- and inter-writer measures of the mutual information. The results show that when pressure is not taken into account, the amount of information is similar in both types of trajectories. Furthermore, even if they share some information, in-air and on-surface trajectories appear to be notably non-redundant.

Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease

OBJECTIVE We present the PaHaW Parkinsons disease handwriting database, consisting of handwriting samples from Parkinsons disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD. METHODS AND MATERIAL The database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting, we investigated new pressure features based on the pressure exerted on the writing surface. To discriminate between PD patients and healthy subjects, three different classifiers were compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM). RESULTS For predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of Pacc=81.3% (sensitivity Psen=87.4% and specificity of Pspe=80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding Pacc=82.5% compared to Pacc=75.4% using kinematic features. CONCLUSION Experimental results showed that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate between PD patients and healthy controls.

Decision Support Framework for Parkinson’s Disease Based on Novel Handwriting Markers

Parkinsons disease (PD) is a neurodegenerative disorder which impairs motor skills, speech, and other functions such as behavior, mood, and cognitive processes. One of the most typical clinical hallmarks of PD is handwriting deterioration, usually the first manifestation of PD. The aim of this study is twofold: (a) to find a subset of handwriting features suitable for identifying subjects with PD and (b) to build a predictive model to efficiently diagnose PD. We collected handwriting samples from 37 medicated PD patients and 38 age- and sex-matched controls. The handwriting samples were collected during seven tasks such as writing a syllable, word, or sentence. Every sample was used to extract the handwriting measures. In addition to conventional kinematic and spatio-temporal handwriting measures, we also computed novel handwriting measures based on entropy, signal energy, and empirical mode decomposition of the handwriting signals. The selected features were fed to the support vector machine classifier with radial Gaussian kernel for automated diagnosis. The accuracy of the classification of PD was as high as 88.13%, with the highest values of sensitivity and specificity equal to 89.47% and 91.89%, respectively. Handwriting may be a valuable marker as a diagnostic and screening tool.

Analysis of in-air movement in handwriting

BACKGROUND AND OBJECTIVE Parkinsons disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting characterized by micrographia and changes in kinematics of handwriting. There is no objective quantitative method of clinical diagnosis of PD. It is thought that PD can only be definitively diagnosed at postmortem, which further highlights the complexities of diagnosis. METHODS We exploit the fact that movement during handwriting of a text consists not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. We used a digitizing tablet to assess both in-air and on-surface kinematic variables during handwriting of a sentence in 37 PD patients on medication and 38 age- and gender-matched healthy controls. RESULTS By applying feature selection algorithms and support vector machine learning methods to separate PD patients from healthy controls, we demonstrated that assessing the in-air/on-surface hand movements led to accurate classifications in 84% and 78% of subjects, respectively. Combining both modalities improved the accuracy by another 1% over the evaluation of in-air features alone and provided medically relevant diagnosis with 85.61% prediction accuracy. CONCLUSIONS Assessment of in-air movements during handwriting has a major impact on disease classification accuracy. This study confirms that handwriting can be used as a marker for PD and can be with advance used in decision support systems for differential diagnosis of PD.

On the focusing of thermal images

In this paper we present a new thermographic image database, suitable for the analysis of automatic focusing measures. This database contains the images of 10 scenes, each of which is represented once for each of 96 different focus positions. Using this database, we evaluate the usefulness of five focus measures with the goal of determining the optimal focus position. Experimental results reveal that the accurate automatic detection of optimal focus position can be achieved with a low computational burden. We also present an acquisition tool for obtaining thermal images. To the best of our knowledge, this is the first study on the automatic focusing of thermal images.

Impact of Parkinson's disease and levodopa on resting state functional connectivity related to speech prosody control

BACKGROUND Impaired speech prosody is common in Parkinsons disease (PD). We assessed the impact of PD and levodopa on MRI resting-state functional connectivity (rs-FC) underlying speech prosody control. METHODS We studied 19 PD patients in the OFF and ON dopaminergic conditions and 15 age-matched healthy controls using functional MRI and seed partial least squares correlation (PLSC) analysis. In the PD group, we also correlated levodopa-induced rs-FC changes with the results of acoustic analysis. RESULTS The PLCS analysis revealed a significant impact of PD but not of medication on the rs-FC strength of spatial correlation maps seeded by the anterior cingulate (p = 0.006), the right orofacial primary sensorimotor cortex (OF_SM1; p = 0.025) and the right caudate head (CN; p = 0.047). In the PD group, levodopa-induced changes in the CN and OF_SM1 connectivity strengths were related to changes in speech prosody. CONCLUSIONS We demonstrated an impact of PD but not of levodopa on rs-FC within the brain networks related to speech prosody control. When only the PD patients were taken into account, the association between treatment-induced changes in speech prosody and changes in rs-FC within the associative striato-prefrontal and motor speech networks was found.

A Criterion for Analysis of Different Sensor Combinations with an Application to Face Biometrics

In this paper, we propose a criterion for pairwise combination of information from different sensors in order to decide how a given pair of sensors is useful for different applications. This criterion is related to the principle of maximum information preservation. We present experimental results for the case of face images at different spectral bands, which allow for the in advance evaluation of the usefulness of different sensor combinations as well as the possibility for crossed-sensor recognition (matching of images acquired in different spectral bands). The criterion that we propose is a generalization of the Fisher score for the case of mutual information, which is measured as the ratio of the interclass information to the intraclass. The score we propose measures the behavior of a pair of sensors either when they are used in combination or when they are used to discriminate between classes. Based on Information Theory measurements, we conclude that the best spectral band combination always contains the thermal image, while the best combination for crossed-sensor recognition is VIS and NIR.

Contribution of different handwriting modalities to differential diagnosis of Parkinson's Disease

In this paper, we evaluate the contribution of different handwriting modalities to the diagnosis of Parkinsons disease. We analyse on-surface movement, in-air movement and pressure exerted on the tablet surface. Especially in-air movement and pressure-based features have been rarely taken into account in previous studies. We show that pressure and in-air movement also possess information that is relevant for the diagnosis of Parkinsons Disease (PD) from handwriting. In addition to the conventional kinematic and spatio-temporal features, we present a group of the novel features based on entropy and empirical mode decomposition of the handwriting signal. The presented results indicate that handwriting can be used as biomarker for PD providing classification performance around 89% area under the ROC curve (AUC) for PD classification.

Interest points as a focus measure

In this paper, we propose a novel focus measure that is based on algorithms for interest point detection, particularly on the Fast Hessian detector. The proposed measure is compared with the energy of image gradient and sum-modified Laplacian that are commonly used as focus measures to test its reliability and performance. The measures have been tested on a newly created database containing 84 images (12 images for seven objects). Our algorithm proved to be a good focus measure satisfying all the requirements described in the paper, in some cases it outperformed the other two measures.

Score fusion in text-dependent speaker recognition systems

According to some significant advantages, the text-dependent speaker recognition is still widely used in biometric systems. These systems are, in comparison with the text-independent, more accurate and resistant against the replay attacks. There are many approaches regarding the text-dependent recognition. This paper introduces a combination of classifiers based on fractional distances, biometric dispersion matcher and dynamic time warping. The first two mentioned classifiers are based on a voice imprint. They have low memory requirements while the recognition procedure is fast. This is advantageous especially in low-cost biometric systems supplied by batteries. It is shown that using the trained score fusion, it is possible to reach successful detection rate equal to 98.98% and 92.19% in case of microphone mismatch. During verification, system reached equal error rate 2.55% and 6.77% when assuming the microphone mismatch. System was tested using Catalan database which consists of 48 speakers (three 3s training samples per speaker).