Christian O'Reilly

Development of a Sigma-Lognormal representation for on-line signatures

This paper proposes an on-line signature representation based on Sigma-Lognormal modeling. It briefly overviews the prior art published on signature modeling and on human movement analysis for handwriting. Then it presents the Sigma-Lognormal paradigm and gives the key steps for the development of a completely automatic parameter extractor for complex human movements. Results of its application on signatures from a proprietary database and from the SVC2004 database are reported and analyzed in regards of the curve fitting quality. Other possible applications and future works are also suggested.

Recent developments in the study of rapid human movements with the kinematic theory: Applications to handwriting and signature synthesis

Human movement modeling can be of great interest for the design of pattern recognition systems relying on the understanding of the fine motor control (such as on-line handwriting recognition or signature verification) as well as for the development of intelligent systems involving in a way or another the processing of human movements. In this paper, we briefly list the different models that have been proposed in order to characterize the handwriting process and focus on a representation involving a vectorial summation of lognormal functions: the Sigma-lognormal model. Then, from a practical perspective, we describe a new stroke extraction algorithm suitable for the reverse engineering of handwriting signals. In the following section it is shown how the resulting representation can be used to study the writer and signer variability. We then report on two joint projects dealing with the automatic generation of synthetic specimens for the creation of large databases. The first application concerns the automatic generation of totally synthetic signature specimens for the training and evaluation of verification performances of automatic signature recognition systems. The second application deals with the synthesis of handwritten gestures for speeding up the learning process in customizable on-line recognition systems to be integrated in electronic pen pads.

Montreal Archive of Sleep Studies: an open-access resource for instrument benchmarking and exploratory research.

Manual processing of sleep recordings is extremely time‐consuming. Efforts to automate this process have shown promising results, but automatic systems are generally evaluated on private databases, not allowing accurate cross‐validation with other systems. In lacking a common benchmark, the relative performances of different systems are not compared easily and advances are compromised. To address this fundamental methodological impediment to sleep study, we propose an open‐access database of polysomnographic biosignals. To build this database, whole‐night recordings from 200 participants [97 males (aged 42.9 ± 19.8 years) and 103 females (aged 38.3 ± 18.9 years); age range: 18–76 years] were pooled from eight different research protocols performed in three different hospital‐based sleep laboratories. All recordings feature a sampling frequency of 256 Hz and an electroencephalography (EEG) montage of 4–20 channels plus standard electro‐oculography (EOG), electromyography (EMG), electrocardiography (ECG) and respiratory signals. Access to the database can be obtained through the Montreal Archive of Sleep Studies (MASS) website (http://www.ceams-carsm.ca/en/MASS), and requires only affiliation with a research institution and prior approval by the applicants local ethical review board. Providing the research community with access to this free and open sleep database is expected to facilitate the development and cross‐validation of sleep analysis automation systems. It is also expected that such a shared resource will be a catalyst for cross‐centre collaborations on difficult topics such as improving inter‐rater agreement on sleep stage scoring.

The lognormal handwriter: learning, performing and declining

The generation of handwriting is a complex neuromotor skill requiring the interaction of many cognitive processes. It aims at producing a message to be imprinted as an ink trace left on a writing medium. The generated trajectory of the pen tip is made up of strokes superimposed over time. The Kinematic Theory of rapid human movements and its family of lognormal models provide analytical representations of these strokes, often considered as the basic unit of handwriting. This paradigm has not only been experimentally confirmed in numerous predictive and physiologically significant tests but it has also been shown to be the ideal mathematical description for the impulse response of a neuromuscular system. This latter demonstration suggests that the lognormality of the velocity patterns can be interpreted as reflecting the behavior of subjects who are in perfect control of their movements. To illustrate this interpretation, we present a short overview of the main concepts behind the Kinematic Theory and briefly describe how its models can be exploited, using various software tools, to investigate these ideal lognormal behaviors. We emphasize that the parameters extracted during various tasks can be used to analyze some underlying processes associated with their realization. To investigate the operational convergence hypothesis, we report on two original studies. First, we focus on the early steps of the motor learning process as seen as a converging behavior toward the production of more precise lognormal patterns as young children practicing handwriting start to become more fluent writers. Second, we illustrate how aging affects handwriting by pointing out the increasing departure from the ideal lognormal behavior as the control of the fine motricity begins to decline. Overall, the paper highlights this developmental process of merging toward a lognormal behavior with learning, mastering this behavior to succeed in performing a given task, and then gradually deviating from it with aging.

Design of a neuromuscular disorders diagnostic system using human movement analysis

This communication summarizes the outcome of our research program on the design of a diagnostic system for neuromuscular disorders based on the analysis of human movement using the Kinematic Theory of Rapid Human Movements. Herein, this design problem is split in sub-problems which are then described. The solutions adopted at each design step are explained. As an example of application, typical results obtained so far for the assessment of the most important modifiable risk factors of brain stroke (diabetes, hypertension, hypercholesterolemia, obesity, cardiac problems, and cigarette smoking) are reported by the means of the area under the receiver operating characteristic curve (AUC).

Automatic sleep spindle detection: benchmarking with fine temporal resolution using open science tools

Sleep spindle properties index cognitive faculties such as memory consolidation and diseases such as major depression. For this reason, scoring sleep spindle properties in polysomnographic recordings has become an important activity in both research and clinical settings. The tediousness of this manual task has motivated efforts for its automation. Although some progress has been made, increasing the temporal accuracy of spindle scoring and improving the performance assessment methodology are two aspects needing more attention. In this paper, four open-access automated spindle detectors with fine temporal resolution are proposed and tested against expert scoring of two proprietary and two open-access databases. Results highlight several findings: (1) that expert scoring and polysomnographic databases are important confounders when comparing the performance of spindle detectors tested using different databases or scorings; (2) because spindles are sparse events, specificity estimates are potentially misleading for assessing automated detector performance; (3) reporting the performance of spindle detectors exclusively with sensitivity and specificity estimates, as is often seen in the literature, is insufficient; including sensitivity, precision and a more comprehensive statistic such as Matthews correlation coefficient, F1-score, or Cohens κ is necessary for adequate evaluation; (4) reporting statistics for some reasonable range of decision thresholds provides a much more complete and useful benchmarking; (5) performance differences between tested automated detectors were found to be similar to those between available expert scorings; (6) much more development is needed to effectively compare the performance of spindle detectors developed by different research teams. Finally, this work clarifies a long-standing but only seldomly posed question regarding whether expert scoring truly is a reliable gold standard for sleep spindle assessment.

An interactive trajectory synthesizer to study outlier patterns in handwriting recognition and signature verification

A software tool has been developed to simulate complex pen tip trajectories and their corresponding velocity profiles based on the kinematic theory of rapid human movements and its lognormal model. The set of equations used to generate the various signals is shortly introduced. The application interface and the functionalities of the main modules are schematized and described. Finally typical simulations results are presented in the context of an interactive comparative analysis of on-line signature data

REM sleep behaviour disorder is associated with lower fast and higher slow sleep spindle densities.

To investigate differences in sleep spindle properties and scalp topography between patients with rapid eye movement sleep behaviour disorder (RBD) and healthy controls, whole‐night polysomnograms of 35 patients diagnosed with RBD and 35 healthy control subjects matched for age and sex were compared. Recordings included a 19‐lead 10–20 electroencephalogram montage and standard electromyogram, electrooculogram, electrocardiogram and respiratory leads. Sleep spindles were automatically detected using a standard algorithm, and their characteristics (amplitude, duration, density, frequency and frequency slope) compared between groups. Topological analyses of group‐discriminative features were conducted. Sleep spindles occurred at a significantly (e.g. t34 = −4.49; P = 0.00008 for C3) lower density (spindles∙min−1) for RBD (mean ± SD: 1.61 ± 0.56 for C3) than for control (2.19 ± 0.61 for C3) participants. However, when distinguishing slow and fast spindles using thresholds individually adapted to the electroencephalogram spectrum of each participant, densities smaller (31–96%) for fast but larger (20–120%) for slow spindles were observed in RBD in all derivations. Maximal differences were in more posterior regions for slow spindles, but over the entire scalp for fast spindles. Results suggest that the density of sleep spindles is altered in patients with RBD and should therefore be investigated as a potential marker of future neurodegeneration in these patients.

Neuromuscular Representation and Synthetic Generation of Handwritten Whiteboard Notes

A fully automatic framework has been introduced recently for neuromuscular representation of complex handwriting patterns, such as gestures, signatures, and words, based on the Kinematic Theory of rapid human movements and its Sigma-Lognormal model. In this paper, we investigate the application of this framework to unconstrained whiteboard notes, taking into account a novel acquisition modality, multiple writers, natural language, and complete text lines. Although these conditions deviate strongly from the previously considered scenario of brief pen movements on tablet computers, we demonstrate that the Sigma-Lognormal model is still able to represent the handwriting accurately. In order to deal with longer handwriting patterns, we propose a robust component-wise representation of text lines that achieves a high model quality. Furthermore, we propose a stroke-wise distortion method to generate synthetic text lines from the Sigma-Lognormal representation of real specimens. For handwriting recognition on the IAM online database, it is demonstrated that the extension of the training set with the proposed synthesis method significantly increases current benchmark results achieved with recurrent neural networks.

Strokes against stroke-strokes for strides

This keynote paper is divided into two parts. On the one hand, it explores how the modeling of pen strokes can be exploited to design biomedical tools allowing the analysis of neuromuscular systems with the objective of developing a diagnostic protocol useful in assessing brain stroke risk factors. On the other hand, it explains how the methodology followed to model a neuromuscular system producing handwriting strokes can be generalized, by means of various strides, to model the Solar System, the Milky Way and the whole Universe. The conducting thread that links up such apparently unrelated pattern recognition problems is the Central Limit Theorem. Feature space analysis of two apparently different complex systems using the central limit theoremDesign of a diagnostic system for neuromuscular disorders using the Kinematic Theory of human movements.The first PR study assessing Brain stroke risk factors from handwriting stoke lognormal features.A new way to bridge the gap between general relativity and quantum mechanics using statistical PR.Description of the physical interactions and the fundamental constants as emerging patterns.