Image analysis – Pattern recognition – Unconstrained handwriting
Reexamination Certificate
2000-03-28
2003-04-08
Johns, Andrew W. (Department: 2621)
Image analysis
Pattern recognition
Unconstrained handwriting
C382S120000, C382S122000, C382S181000, C382S187000, C382S205000, C382S278000, C382S306000, C073S865400, C434S155000
Reexamination Certificate
active
06546134
ABSTRACT:
BACKGROUND OF THE INVENTION
a) Field of the Invention
This invention relates to a biometric machine method for quantitative assessment of fine motor control of human individuals through the monitoring of changes in cursive writing dynamics. Such quantitative assessment uses Correlation Function Analysis of handwriting dynamic signals, such as acceleration and pressure, in time domain, and returns values of criteria for stability, smoothness and synchronization of handwriting motions.
b) Description of the Prior Art
It has been very difficult to identify individuals with unset Parkinson's and other neurological diseases because of subtlety of the early symptoms It is therefore difficult to identify and adequately characterize the unset of fine motor control deficiencies in such individuals. It has also been difficult to assess impairment or impediment in fine motor control due to the influence of alcohol and drug substances, or from environmentally caused distress. Handwriting is a complex cognitive motor skill. Since handwriting is usually a well-learned skill that is generally used on a daily basis, the motor behavioral aspects of handwriting are theoretically interesting and practically important particularly in the early identification and assessment of fine motor control impairment as a result of the above-mentioned problem. Thus, research based on the changes in handwriting dynamics have been considered in an effort to better understand, identify, and assess human fine motor control. In the recent past there has been a growing interest in research in fine motor control of humans through analysis of handwriting dynamics. Several publications in the field of Neurology represent this trend. For example, David Margolin and Alan Wing, in their paper “
Agraphia and Micrographia: Clinical Manifestations of Motor Programming and Performance Disorders
” published in Acta Psychologica, 1983, presented the results of research of acquired disorders of handwriting. They compared acquired agraphia due to cerebrovascular accident to micrographia produced by the Parkinsonian patients. They stated, “existing reports of apraxic agraphia . . . do not provide much information about handwriting control from an information processing standpoint, although they can be useful in determining the anatomical localization of handwriting control. Overall, the reported cases of apraxic agraphia indicate that handwriting can be disrupted without affecting other motor skills.” Parkinsonism, on the contrary, “affects all voluntary movements, but handwriting appears to be particularly vulnerable, in that it is frequently the first manifestation of this disease.” And they conclude “if handwriting is indeed sensitive to disturbances of the extrapyramidal motor system then a quantitative analysis of writing could serve as a useful tool in evaluating diseases which affect this system and provide insights into dynamics behind these handwriting changes.” The present invention provides such new channels of information about motor control of handwriting and will shed light on the complex mechanisms of fine motor control.
In the research of handwriting generation by R. Plamondon published in Acta Psychologica 82 (1993), the author states “strokes must be superimposed to generate fluent handwriting. This is in accordance with a basic psychophysical phenomenon often reported in motor control: the handwriting generation process, like many other types of movements, is not exclusively sequential, and very often advanced preparation of the forthcoming stroke is done in parallel with the execution of the actual stroke. . . . In other words, the basic strokes are hidden in the signal.” In conclusion, Plamondon suggests the method to extract strokes by performing “an analysis-by-synthesis experiment, with the help of the proper impulse response for each stroke” and the use of quite complicated models of reconstructing the curvilinear velocity profiles defined by a log-normal equations. However, Plamondon presents a method for mathematical modeling of individual strokes and fails to teach a system for analysis of sequences of strokes. The extraction of single strikes and their modeling and analysis, as proposed by Plamondon, would result in loosing important information about behavior of strokes in their sequence. In addition to that, identification and high precision modeling of individual strokes represents very difficult and even unfeasible task because of greatest complexity of each handwriting movement where numerous elements of the central control and neuromuscular systems are involved in their performance. The present invention teaches a new analysis of the handwriting dynamic signals as indivisible collections of strokes intermittent with time distortions, contrary to considering individual strokes.
E. Parkins in his article “
Cerebellum and Cerebrum in Adaptive Control and Cognition: a Review
” published in Biological Cybernetics, 77 (1997), explores the relative roles of the cerebrum and cerebellum in adaptive control. Parkins makes an interesting observation: “Voluntary movements may first be performed and controlled by relying on feedback from sensory organs, but after some practice the same movement will be performed without feedback, the movement being performed more quickly and more automatically with less conscious effort. Here, practice converts the mode of voluntary movement from feedback to feed forward. The cerebellum operating as an adaptive feedforward system may be inserted in parallel to the cerebral cortex operating as an executive feedback-control system.” The present invention, through measurement and assessment of handwriting dynamics representing both automatic, such as signature, and cognitively controlled cursive writing, such as “llll”, “lmlm”, “lele”, etc., provides valuable and previously unavailable information about the role and interplay of cerebellum and cerebrum in adaptive motor control and cognition.
Between 1994 and 1997, a joint European project, MIAMI(Multimodal Integration for Advanced Multimedia Interfaces) for the universities of The Netherlands, Germany, Denmark, and Italy was conducted for extensive research of human handwriting. The report of MIAMI resents the following observation: “Contrary to speech, cursive handwriting is not an innate neural function, and must be trained over several years. During the training process, handwriting evolves from a slow feedback process involving active attention and eye-hand coordination to a fast automatic and ballistic process. The atomic movement unit in cursive handwriting is a stroke, which is a movement trajectory bounded by two points of high curvature and a corresponding dip in the tangential movement velocity. The typical modal stroke duration is of the order of 100 msec, and varies less for increased movement amplitudes than one would expect. For a large range, writers exert an increased force in order to maintain a preferred rhythm of movement. Once “fired” cortically, such a stroke cannot be corrected by visual feedback”.
Some early publications in the field of physiology also represent interest for the present invention. In 1864, Russian physiologist K. I. Barr published his work in which he first introduced the concept of “biological quantum of time” which was further developed by German researchers J. V. Uexkull and G. Kriszat, who called it “physiological moment” in their paper “Streifzuge durch die Umwelten von Tieren und Menschen”, 1970. It is very plausible to assume that the stroke, the atomic movement unit in cursive handwriting, is a spatial representation of the biological quantum. The biological quantum of time is estimated to be 100 to 180 msec. In addition to the literature, several devices have been developed to measure tremor. The primary known devices include:
1. Potentiometers to indicate motion of an extremity. They use a mechanical linkage, similar to an articulated dentists' drill arm with a potentiometer at each joint. However, such linkages very greatly restrict the physical motion of the extre
Landau Alexander
Shrairman Ruth
Johns Andrew W.
Margolis Donald W.
Mariam Daniel G.
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