Theory Of Rapid Human Movements Research Articles

Sit-to-Stand Test for Neurodegenerative Diseases Video Classification

In this extended version of this paper, an automatic video diagnosis system for dementia classification is presented. Starting from video recordings of patients and control subjects, performing sit-to-stand test, the designed system is capable of extracting relevant patterns for binary discern patients with dementia from healthy subjects. The original system achieved an accuracy 0.808 by using the rigorous inter-patient separation scheme especially suited for medical purposes. This separation scheme provides the use of some people for training and others, different, people for testing. The implementation of features from the kinematic theory of rapid human movement and its sigma-lognormal model together with classic features increased the overall accuracy of the system to 0.947 F1 score. In addition, multi-class classification was performed with the aim of classifying neurodegenerative disease severities. This work is an original and pioneering work on sit-to-stand video classification for neurodegenerative diseases, its novelties are on phases segmentation, experimental setup and the application of kinematic theory of rapid human movements to sit-to-stand videos for neurodegenerative disease assessment.

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.

Central and Peripheral Shoulder Fatigue Pre-screening Using the Sigma-Lognormal Model: A Proof of Concept.

BackgroundClinical tests for detecting central and peripheral shoulder fatigue are limited. The discrimination of these two types of fatigue is necessary to better adapt recovery intervention. The Kinematic Theory of Rapid Human Movements describes the neuromotor impulse response using lognormal functions and has many applications in pathology detection. The ideal motor control is modeled and a change in the neuromuscular system is reflected in parameters extracted according to this theory.ObjectiveThe objective of this study was to assess whether a shoulder neuromuscular fatigue could be detected through parameters describing the theory, if there is the possibility to discriminate central from peripheral fatigue, and which handwriting test gives the most relevant information on fatigue.MethodsTwenty healthy participants performed two sessions of fast stroke handwriting on a tablet, before and after a shoulder fatigue. The fatigue was in internal rotation for one session and in external rotation during the other session. The drawings consisted of simple strokes, triangles, horizontal, and vertical oscillations. Parameters of these strokes were extracted according to the Sigma–Lognormal model of the Kinematic Theory. The evolution of each participant was analyzed through a U-Mann–Whitney test for individual comparisons. A Hotelling’s T2-test and a U-Mann–Whitney test were also performed on all participants to assess the group evolution after fatigue. Moreover, a correlation among parameters was calculated through Spearman coefficients to assess intrinsic parameters properties of each handwriting test.ResultsCentral and peripheral parameters were statistically different before and after fatigue with a possibility to discriminate them. Participants had various responses to fatigue. However, when considering the group, parameters related to the motor program execution showed significant increase in the handwriting tests after shoulder fatigue. The test of simple strokes permits to know more specifically where the fatigue comes from, whereas the oscillations tests were the most sensitive to fatigue.ConclusionThe results of this study suggest that the Sigma–Lognormal model of the Kinematic Theory is an innovative approach for fatigue detection with discrimination between the central and peripheral systems. Overall, there is a possibility to implement the setting for clinics and sports personalized follow-up.

Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements

Neurodegenerative diseases are particular diseases whose decline can partially or completely compromise the normal course of life of a human being. In order to increase the quality of patient's life, a timely diagnosis plays a major role. The analysis of neurodegenerative diseases, and their stage, is also carried out by means of gait analysis. Performing early stage neurodegenerative disease assessment is still an open problem. In this paper, the focus is on modeling the human gait movement pattern by using the kinematic theory of rapid human movements and its sigma-lognormal model. The hypothesis is that the kinematic theory of rapid human movements, originally developed to describe handwriting patterns, and used in conjunction with other spatio-temporal features, can discriminate neurodegenerative diseases patterns, especially in early stages, while analyzing human gait with 2D cameras. The thesis empirically demonstrates its effectiveness in describing neurodegenerative patterns, when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. The solution developed achieved 99.1% of accuracy using velocity-based, angle-based and sigma-lognormal features and left walk orientation.

Kinematic analysis of fast pen strokes in children with ADHD

In this study, we aimed to determine whether a new measure of fine motor skills, the Pen Stroke Test (PST), can discriminate between children with and without attention-deficit/hyperactivity disorder (ADHD). Twelve children with ADHD and 12 controls age 8–11 were asked to produce handwriting strokes on a digitizer. The sigma-lognormal model derived from the Kinematic Theory of rapid human movements was used to analyze the strokes. Standard measurements of fine motor skills and handwriting were also obtained. Children with ADHD demonstrated poorer motor planning (t0, D) and execution (nbLog) and greater variability in motor control (SNR/nbLog) than did controls. Parameters extracted from the PST were significantly correlated with performance on other motor and handwriting measures. This study provides preliminary evidence that the PST may be useful as a tool for rapidly detecting motor skill problems in the context of ADHD.

Active detection of age groups based on touch interaction

This article studies user classification into children and adults according to their interaction with touchscreen devices. The authors analyse the performance of two sets of features derived from the sigma-lognormal theory of rapid human movements and global characterisation of touchscreen interaction. The authors propose an active detection approach aimed to continuously monitor the user patterns. The experimentation is conducted on a publicly available database with samples obtained from 89 children between 3 and 6 years old and 30 adults. The authors have used support vector machines algorithm to classify the resulting features into age groups. The sets of features are fused at the score level using data from smartphones and tablets. The results, with correct classification rates over 96%, show the discriminative ability of the proposed neuromotor-inspired features to classify age groups according to the interaction with touch devices. In the active detection set-up, the authors' method is able to identify a child using only four gestures in average.

Detecting age groups using touch interaction based on neuromotor characteristics

A new parental control method to prevent unauthorised usage of touch devices by kids is proposed. The impact of rapidly advancing technology on the developing child has seen an increase exposition to new forms of danger. Studies reveal that 97% of US children under the age of four use mobile devices. A reliable and efficient method to prevent the use of touch devices by preschool children is proposed. The proposed method is based on the analysis of the neuromotor characteristics of the users according to the decomposition of simple drag and drop tasks using the kinematic theory of rapid human movements. The experimentation is conducted on a publicly available database with samples obtained from 89 children between 3 and 6 years old and 30 adults. The results are compared with an existent system based only on task time and accuracy. Finally, both systems are combined at score level to achieve better performances. The results, with correct classification rates over 96% in the combined system, show the discriminative ability of the proposed neuromotor-inspired features and the possibility of combining this system with others to improve their final performance.

A sigma-lognormal model-based approach to generating large synthetic online handwriting sample databases

This article describes a methodology to generate a large database of synthetic samples from a small set of original online handwriting specimens. The overall paradigm is based on the Kinematic Theory of rapid human movements and its sigma-lognormal model. The principal contributions of the present study include (i) development of a strategy for sigma-lognormal model-based generation of synthetic samples from real online handwriting samples of arbitrary scripts captured by arbitrary relevant devices and (ii) verification of the structural similarities, including the naturalness of such synthetic prototypes, through various human perception experiments, computer evaluations and statistical hypothesis testing. A database consisting of a large number of online synthetic handwritten word samples is used to train and evaluate the performance of three existing automatic online handwriting recognition systems. Training based on a combined set of original and synthetic samples improves the recognition accuracies on the test set. A combined training set is useful irrespective of the nature of the feature set used (online, offline or combined). Although the proposed method has primarily been developed and applied to the design of an online handwriting sample database of a popular Indian script, Bangla, it can be applied to the generation of large databases of any arbitrary script for example: English, Chinese and Arabic.

Signature Verification Based on the Kinematic Theory of Rapid Human Movements

When using tablet computers, smartphones, or digital pens, human users perform movements with a stylus or their fingers that can be analyzed by the kinematic theory of rapid human movements. In this paper, we present a user-centered system for signature verification that performs such a kinematic analysis to verify the identity of the user. It is one of the first systems that is based on a direct comparison of the elementary neuromuscular strokes which are detected in the handwriting. Taking into account the number of strokes, their similarity, and their timing, the string edit distance is employed to derive a dissimilarity measure for signature verification. On several benchmark datasets, we demonstrate that this neuromuscular analysis is complementary to a well-established verification using dynamic time warping. By combining both approaches, our verifier is able to outperform current state-of-the-art results in on-line signature verification.

Temporal evolution in synthetic handwriting

New methods for generating synthetic handwriting images for biometric applications have recently been developed. The temporal evolution of handwriting from childhood to adulthood is usually left unexplored in these works. This paper proposes a novel methodology for including temporal evolution in a handwriting synthesizer by means of simplifying the text trajectory plan and handwriting dynamics. This is achieved through a tailored version of the kinematic theory of rapid human movements and the neuromotor inspired handwriting synthesizer. The realism of the proposed method has been evaluated by comparing the temporal evolution of real and synthetic samples both quantitatively and subjectively. The quantitative test is based on a visual perception algorithm that compares the letter variability and the number of strokes in the real and synthetic handwriting produced at different ages. In the subjective test, 30 people are asked to evaluate the perceived realism of the evolution of the synthetic handwriting.

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 ( g 3), 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, and g 3 will create a model of that gesture. Then, by introducing local and global perturbations to the model parameters, g 3 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 5years. 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.

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.

Synthetic on-line signature generation. Part I: Methodology and algorithms

The theoretical framework and algorithms of a novel method for the generation of synthetic on-line signatures are presented. This model-based approach combines the spectral analysis of real signatures with the Kinematic Theory of rapid human movements in order to generate totally synthetic specimens. Two different algorithms are also described in order to produce duplicated samples from the synthetic master signatures, so that the generation scheme as a whole is able to produce in a complete automatic fashion huge synthetic databases. Typical examples of synthetic specimens are presented to highlight their human-like appearance. The validation protocol and the test results are presented and discussed in a companion paper.

Synthetic on-line signature generation. Part II: Experimental validation

A novel method for the generation of synthetic on-line signatures based on the spectral analysis and the Kinematic Theory of rapid human movements, was presented in Part I of this series of two papers. In the present paper, the experimental framework used for the validation of the novel approach is described. The validation protocol, which uses different development and test sets in order to achieve unbiased results, includes the comparison of real and synthetic databases in terms of (i) visual appearance, (ii) statistical information, and (iii) performance evaluation of three competitive and totally different verification systems. The experimental results show the high similarity existing between synthetically generated and humanly produced samples, and the great potential of the method for the study of the signature trait. ► Validation experimental results of a novel synthetic signature generation method. ► Protocol replicable and consistent. ► It may be applied to validate synthetic databases of different traits. ► Results show remarkably high resemblance of synthetic signatures with real samples. ► Very high potential for performance estimation.

A New Algorithm and System for the Characterization of Handwriting Strokes with Delta-Lognormal Parameters

In this paper, we present a new analytical method for estimating the parameters of Delta-Lognormal functions and characterizing handwriting strokes. According to the Kinematic Theory of rapid human movements, these parameters contain information on both the motor commands and the timing properties of a neuromuscular system. The new algorithm, called XZERO, exploits relationships between the zero crossings of the first and second time derivatives of a lognormal function and its four basic parameters. The methodology is described and then evaluated under various testing conditions. The new tool allows a greater variety of stroke patterns to be processed automatically. Furthermore, for the first time, the extraction accuracy is quantified empirically, taking advantage of the exponential relationships that link the dispersion of the extraction errors with its signal-to-noise ratio. A new extraction system which combines this algorithm with two other previously published methods is also described and evaluated. This system provides researchers involved in various domains of pattern analysis and artificial intelligence with new tools for the basic study of single strokes as primitives for understanding rapid human movements.

DETERMINISTIC AND EVOLUTIONARY EXTRACTION OF DELTA-LOGNORMAL PARAMETERS: PERFORMANCE COMPARISON

A theory, called the Kinematic Theory of Rapid Human Movement, was proposed a few years ago to analyze rapid human movements, called the Kinematic Theory of Rapid Human Movements, based on a delta-lognormal equation that globally describes the basic properties of the velocity profiles of an end-effector using seven parameters. This realistic model has been very useful for proposing original solutions to various pattern recognition problems (signature segmentation and verification, handwriting analysis and synthesis, etc.). Most of these applications rely on the use of an efficient algorithm to extract the delta-lognormal parameters from real data with the best possible fit. In this paper, we compare two such algorithms: a deterministic one, based on nonlinear regression, and a Breeder Genetic algorithm. The performance of these two algorithms and of their combinations are compared using the same artificial database, composed of analytical delta-lognormal profiles and their noisy versions (20 dB SNR). In the free-noise case, the analysis of the experimental results shows that the deterministic approach leads to better results than the evolutionary one, while under the extremely noisy conditions selected, the evolutionary approach seems to be less sensitive to noise, but is nevertheless less successful than the deterministic search.

Learning handwriting with pen-based systems: computational issues

This paper introduces the theoretical foundation for the development of a pen-based system dedicated to helping to teach handwriting in primary schools. Knowledge given by a kinematic theory of rapid human movements is used. The system proposed includes a letter model generator which is used to create letter shapes with a human-like kinematics. The system generates feedback to pupils after a multilevel analysis of the handwriting. The analysis presented deals with shape conformity, shape error identification, fluency analysis and kinematic parameter evaluation. Discussion on how fluency measurement and error quantification can be useful in developing a learning metric is also presented.

The Generation of Oriental Characters

We recently developed a general theory of rapid human movements and applied it to the generation of western handwriting. The goal of this paper is to summarize the key concepts behind the so-called vectorial delta-lognormal model that results from this theory and to show how this model could be used for Chinese character analysis and processing.

A kinematic theory of rapid human movements: Part III. Kinetic outcomes.

This paper describes the kinematic and kinetic properties of simple rapid movements using a single and unique framework based on a delta-lognormal law (Plamondon 1993a,b, 1995a,b). Predictions concerning isotonic measurements are made using the properties of acceleration profiles, as described by the first time derivative of the delta-lognormal law. Predictions dealing with isometric measurements are directly analyzed using the delta-lognormal law, after demonstrating the experimental equivalence between isometric forces and virtual velocity profiles. The theory is also used to make statistical predictions about the variability of numerous kinematic and kinetic variables. The overall approach can be viewed as if, at some level of representation, the central nervous system were planning, executing and evaluating simple rapid movements in terms of momentum and energy instead of forces. The unifying perspective provided by the theory constitutes a powerful tool with which to study and analyze movements under numerous experimental conditions, using a single analytical law.