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

Sigma-Lognormal Modeling of Speech

Human movement studies and analyses have been fundamental in many scientific domains, ranging from neuroscience to education, pattern recognition to robotics, health care to sports, and beyond. Previous speech motor models were proposed to understand how speech movement is produced and how the resulting speech varies when some parameters are changed. However, the inverse approach, in which the muscular response parameters and the subject’s age are derived from real continuous speech, is not possible with such models. Instead, in the handwriting field, the kinematic theory of rapid human movements and its associated Sigma-lognormal model have been applied successfully to obtain the muscular response parameters. This work presents a speech kinematics-based model that can be used to study, analyze, and reconstruct complex speech kinematics in a simplified manner. A method based on the kinematic theory of rapid human movements and its associated Sigma-lognormal model are applied to describe and to parameterize the asymptotic impulse response of the neuromuscular networks involved in speech as a response to a neuromotor command. The method used to carry out transformations from formants to a movement observation is also presented. Experiments carried out with the (English) VTR-TIMIT database and the (German) Saarbrucken Voice Database, including people of different ages, with and without laryngeal pathologies, corroborate the link between the extracted parameters and aging, on the one hand, and the proportion between the first and second formants required in applying the kinematic theory of rapid human movements, on the other. The results should drive innovative developments in the modeling and understanding of speech kinematics.

Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health

Online handwritten analysis presents many applications in e-security, signature biometrics being the most popular but not the only one. Handwriting analysis also has an important set of applications in e-health. Both kinds of applications (e-security and e-health) have some unsolved questions and relations among them that should be addressed in the next years. We summarize the state of the art and applications based on handwriting signals. Later on, we focus on the main achievements and challenges that should be addressed by the scientific community, providing a guide for future research. Among all the points discussed in this article, we remark the importance of considering security, health, and metadata from a joint perspective. This is especially critical due to the risks inherent when using these behavioral signals.

iDeLog: Iterative Dual Spatial and Kinematic Extraction of Sigma-Lognormal Parameters

The Kinematic Theory of rapid movements and its associated Sigma-Lognormal model have been extensively used in a large variety of applications. While the physical and biological meaning of the model have been widely tested and validated for rapid movements, some shortcomings have been detected when it is used with continuous long and complex movements. To alleviate such drawbacks, and inspired by the motor equivalence theory and a conceivable visual feedback, this paper proposes a novel framework to extract the Sigma-Lognormal parameters, namely iDeLog. Specifically, iDeLog consists of two steps. The first one, influenced by the motor equivalence model, separately derives an initial action plan defined by a set of virtual points and angles from the trajectory and a sequence of lognormals from the velocity. In the second step, based on a hypothetical visual feedback compatible with an open-loop motor control, the virtual target points of the action plan are iteratively moved to improve the matching between the observed and reconstructed trajectory and velocity. During experiments conducted with handwritten signatures, iDeLog obtained promising results as compared to the previous development of the Sigma-Lognormal.

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Studying the effects of neurodegeneration on handwriting has emerged as an interdisciplinary research topic and has attracted considerable interest from psychologists to neuroscientists and from physicians to computer scientists. The complexity of handwriting, in fact, appears to be sensitive to age-related impairments in cognitive functioning; thus, analyzing handwriting in elderly people may facilitate the diagnosis and monitoring of these impairments. A large body of knowledge has been collected in the last thirty years thanks to the advent of new technologies which allow researchers to investigate not only the static characteristics of handwriting but also especially the dynamic aspects of the handwriting process. The present paper aims at providing an overview of the most relevant literature investigating the application of dynamic handwriting analysis in neurodegenerative disease assessment. The focus, in particular, is on Parkinon’s disease (PD) and Alzheimer’s disease (AD), as the two most widespread neurodegenerative disorders. More specifically, the studies taken into account are grouped in accordance with three main research questions: disease insight, disease monitoring, and disease diagnosis. The net result is that dynamic handwriting analysis is a powerful, noninvasive, and low-cost tool for real-time diagnosis and follow-up of PD and AD. In conclusion of the paper, open issues still demanding further research are highlighted.

Dynamic Signature Verification System Based on One Real Signature

The dynamic signature is a biometric trait widely used and accepted for verifying a person's identity. Current automatic signature-based biometric systems typically require five, ten, or even more specimens of a person's signature to learn intrapersonal variability sufficient to provide an accurate verification of the individual's identity. To mitigate this drawback, this paper proposes a procedure for training with only a single reference signature. Our strategy consists of duplicating the given signature a number of times and training an automatic signature verifier with each of the resulting signatures. The duplication scheme is based on a sigma lognormal decomposition of the reference signature. Two methods are presented to create human-like duplicated signatures: the first varies the strokes' lognormal parameters (stroke-wise) whereas the second modifies their virtual target points (target-wise). A challenging benchmark, assessed with multiple state-of-the-art automatic signature verifiers and multiple databases, proves the robustness of the system. Experimental results suggest that our system, with a single reference signature, is capable of achieving a similar performance to standard verifiers trained with up to five signature specimens.

Capturing the Cranio-Caudal Signature of a Turn with Inertial Measurement Systems: Methods, Parameters Robustness and Reliability

Background

Turning is a challenging mobility task requiring coordination and postural stability. Optimal turning involves a cranio-caudal sequence (i.e., the head initiates the motion, followed by the trunk and the pelvis), which has been shown to be altered in patients with neurodegenerative diseases, such as Parkinson’s disease as well as in fallers and frails. Previous studies have suggested that the cranio-caudal sequence exhibits a specific signature corresponding to the adopted turn strategy. Currently, the assessment of cranio-caudal sequence is limited to biomechanical labs which use camera-based systems; however, there is a growing trend to assess human kinematics with wearable sensors, such as attitude and heading reference systems (AHRS), which enable recording of raw inertial signals (acceleration and angular velocity) from which the orientation of the platform is estimated. In order to enhance the comprehension of complex processes, such as turning, signal modeling can be performed.

Aim

The current study investigates the use of a kinematic-based model, the sigma-lognormal model, to characterize the turn cranio-caudal signature as assessed with AHRS.

Methods

Sixteen asymptomatic adults (mean age = 69.1 ± 7.5 years old) performed repeated 10-m Timed-Up-and-Go (TUG) with 180° turns, at varying speed. Head and trunk kinematics were assessed with AHRS positioned on each segments. Relative orientation of the head to the trunk was then computed for each trial and relative angular velocity profile was derived for the turn phase. Peak relative angle (variable) and relative velocity profiles modeled using a sigma-lognormal approach (variables: Neuromuscular command amplitudes and timing parameters) were used to extract and characterize the cranio-caudal signature of each individual during the turn phase.

Results

The methodology has shown good ability to reconstruct the cranio-caudal signature (signal-to-noise median of 17.7). All variables were robust to speed variations (p > 0.124). Peak relative angle and commanded amplitudes demonstrated moderate to strong reliability (ICC between 0.640 and 0.808).

Conclusion

The cranio-caudal signature assessed with the sigma-lognormal model appears to be a promising avenue to assess the efficiency of turns.

A Behavioral Handwriting Model for Static and Dynamic Signature Synthesis

The synthetic generation of static handwritten signatures based on motor equivalence theory has been recently proposed for biometric applications. Motor equivalence divides the human handwriting action into an effector dependent cognitive level and an effector independent motor level. The first level has been suggested by others as an engram, generated through a spatial grid, and the second has been emulated with kinematic filters. Our paper proposes a development of this methodology in which we generate dynamic information and provide a unified comprehensive synthesizer for both static and dynamic signature synthesis. The dynamics are calculated by lognormal sampling of the 8-connected continuous signature trajectory, which includes, as a novelty, the pen-ups. The forgery generation imitates a signature by extracting the most perceptually relevant points of the given genuine signature and interpolating them. The capacity to synthesize both static and dynamic signatures using a unique model is evaluated according to its ability to adapt to the static and dynamic signature inter- and intra-personal variability. Our highly promising results suggest the possibility of using the synthesizer in different areas beyond the generation of unlimited databases for biometric training.

Benchmarking desktop and mobile handwriting across COTS devices: The e-BioSign biometric database

This paper describes the design, acquisition process and baseline evaluation of the new e-BioSign database, which includes dynamic signature and handwriting information. Data is acquired from 5 different COTS devices: three Wacom devices (STU-500, STU-530 and DTU-1031) specifically designed to capture dynamic signatures and handwriting, and two general purpose tablets (Samsung Galaxy Note 10.1 and Samsung ATIV 7). For the two Samsung tablets, data is collected using both pen stylus and also the finger in order to study the performance of signature verification in a mobile scenario. Data was collected in two sessions for 65 subjects, and includes dynamic information of the signature, the full name and alpha numeric sequences. Skilled forgeries were also performed for signatures and full names. We also report a benchmark evaluation based on e-BioSign for person verification under three different real scenarios: 1) intra-device, 2) inter-device, and 3) mixed writing-tool. We have experimented the proposed benchmark using the main existing approaches for signature verification: feature- and time functions-based. As a result, new insights into the problem of signature biometrics in sensor-interoperable scenarios have been obtained, namely: the importance of specific methods for dealing with device interoperability, and the necessity of a deeper analysis on signatures acquired using the finger as the writing tool. This e-BioSign public database allows the research community to: 1) further analyse and develop signature verification systems in realistic scenarios, and 2) investigate towards a better understanding of the nature of the human handwriting when captured using electronic COTS devices in realistic conditions.

Gestures à Go Go

Training a high-quality gesture recognizer requires providing a large number of examples to enable good performance on unseen, future data. However, recruiting participants, data collection, and labeling, etc., necessary for achieving this goal are usually time consuming and expensive. Thus, it is important to investigate how to empower developers to quickly collect gesture samples for improving UI usage and user experience. In response to this need, we introduce Gestures à Go Go (g3), a web service plus an accompanying web application for bootstrapping stroke gesture samples based on the kinematic theory of rapid human movements. The user only has to provide a gesture example once, andg3 will create a model of that gesture. Then, by introducing local and global perturbations to the model parameters,g3 generates from tens to thousands of synthetic human-like samples. Through a comprehensive evaluation, we show that synthesized gestures perform equally similar to gestures generated by human users. Ultimately, this work informs our understanding of designing better user interfaces that are driven by gestures.

Combining sigma-lognormal modeling and classical features for analyzing graphomotor performances in kindergarten children

This paper investigates the advantage of using the kinematic theory of rapid human movements as a complementary approach to those based on classical dynamical features to characterize and analyze kindergarten children's ability to engage in graphomotor activities as a preparation for handwriting learning. This study analyzes nine different movements taken from 48 children evenly distributed among three different school grades corresponding to pupils aged 3, 4, and 5 years. On the one hand, our results show that the ability to perform graphomotor activities depends on kindergarten grades. More importantly, this study shows which performance criteria, from sophisticated neuromotor modeling as well as more classical kinematic parameters, can differentiate children of different school grades. These criteria provide a valuable tool for studying children's graphomotor control learning strategies. On the other hand, from a practical point of view, it is observed that school grades do not clearly reflect pupils' graphomotor performances. This calls for a large-scale investigation, using a more efficient experimental design based on the various observations made throughout this study regarding the choice of the graphic shapes, the number of repetitions and the features to analyze.

Modeling the lexical morphology of Western handwritten signatures

A handwritten signature is the final response to a complex cognitive and neuromuscular process which is the result of the learning process. Because of the many factors involved in signing, it is possible to study the signature from many points of view: graphologists, forensic experts, neurologists and computer vision experts have all examined them. Researchers study written signatures for psychiatric, penal, health and automatic verification purposes. As a potentially useful, multi-purpose study, this paper is focused on the lexical morphology of handwritten signatures. This we understand to mean the identification, analysis, and description of the signature structures of a given signer. In this work we analyze different public datasets involving 1533 signers from different Western geographical areas. Some relevant characteristics of signature lexical morphology have been selected, examined in terms of their probability distribution functions and modeled through a General Extreme Value distribution. This study suggests some useful models for multi-disciplinary sciences which depend on handwriting signatures.

Using kinematic analysis of movement to predict the time occurrence of an evoked potential associated with a motor command

This article presents an exploratory study investigating the possibility of predicting the time occurrence of a motor event related potential (ERP) from a kinematic analysis of human movements. Although the response-locked motor potential may link the ERP components to the recorded response, to our knowledge no previous attempt has been made to predict a priori (i.e. before any contact with the electroencephalographic data) the time occurrence of an ERP component based only on the modeling of an overt response. The proposed analysis relies on the delta-lognormal modeling of velocity, as proposed by the kinematic theory of rapid human movement used in several studies of motor control. Although some methodological aspects of this technique still need to be fine-tuned, the initial results showed that the model-based kinematic analysis allowed the prediction of the time occurrence of a motor command ERP in most participants in the experiment. The average map of the motor command ERPs showed that this signal was stronger in electrodes close to the contra-lateral motor area (Fz, FCz, FC1, and FC3). These results seem to support the claims made by the kinematic theory that a motor command is emitted at time t(0), the time reference parameter of the model. This article proposes a new time marker directly associated with a cerebral event (i.e. the emission of a motor command) that can be used for the development of new data analysis methodologies and for the elaboration of new experimental protocols based on ERP.