EP0681725A4 - Image communication apparatus. - Google Patents

Abstract

Communication apparatus for hand imaging including apparatus (11) for sensing features of hand imaging (10) of an individual which features are highly characteristic of the individual but which also contain information relating to images represented thereby and apparatus (13) for providing a non-individual dependent output (14) indicating the images in response to the sensed features.

Description

IMAGE COMMUNICATION APPARATUS

The present invention relates to handwriting and draw¬ ing communication devices generally.

There exists a significant amount of activity in the field of on-line handwriting analysis. The prior art current to 1990 is reviewed in "The State of the Art in On-Line Handwriting Recognition" by Charles C. Tappert et al, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 8, Au¬ gust, 1990.

Generally speaking, on-line handwriting analysis is currently employed for two distinct applications identity verifi¬ cation and input of handwritten letters and numbers into a computer. These two applications have sharply contrasting opera¬ tional requirements and goals. Handwriting analysis for identity verification senses features of handwriting which are distinct for each individual and thus can be used to unambiguously identi¬ fy a given individual. In contrast, handwriting analysis for alphanumeric input to a computer seeks to minimize the effect of the very features which are important for identity verification and to concentrate on universal handwriting characteristics which can be associated with given symbols independently of the indi¬ vidual writer.

Currently existing and proposed systems providing handwriting analysis for alphanumeric input to a computer are generally geared towards recognition of how a symbol looks rather than how it is created. Accordingly, such systems employ digitiz¬ ers or graphic tablets.

Signature verification systems, on the other hand, attempt to identify biometric characteristics of the writer and employ indications such as pressure and acceleration during writing.

U.S. Patent 4,345,239 employs pen acceleration for use in a signature verification system. U.S. Patent 5,054,088 employs both acceleration and pressure data characteristics of handwrit¬ ing for identity verification. As indicated by the above patents, pen acceleration is employed for signature verification because it is a personal feature, characteristic of each individual. Accordingly, pen acceleration has not been employed for communi¬ cation of hand imaging.

U.S. Patent 4,817,034 describes a computerized hand¬ writing duplication system employing a digitizer pad. U.S. Patent 4,641,354 describes apparatus for recognizing and display¬ ing handwritten characters and figures in which unrecognized stroke information remains on the display screen. U.S. Patent 4,715,102 describes a process and apparatus involving pattern recognition. U.S. Patent 4,727,588 describes a system for auto¬ matic adjustment and editing of a handwritten text image, which preserves format information in a handwritten text. U.S. Patent 4,703,511 describes a writing input and dynamics regeneration device wherein a time dependent code is embedded in a writing path.

The present invention seeks to provide improved hand¬ writing and drawing communication apparatus. For convenience and conciseness, the term "hand imaging" will be used throughout the specification and claims to denote handwriting activity as well as drawing activity and any other two or three dimensional image generating hand movements.

There is thus provided in accordance with a preferred embodiment of the present invention communication apparatus for hand imaging including apparatus for sensing features of hand imaging of an individual which features are highly characteristic of the individual but which also contain information relating to images represented thereby and apparatus for providing a non- individual dependent output indicating the images in response to the sensed features.

The phrase "non-individual dependent output" denotes an output which contains non-individual characteristics as well as, optionally, individual characteristics. Non-individual charac¬ teristics may include a geometric representation of a hand imag¬ ing product. The non-individual dependent output is typically in a form that can be communicated and read universally, for exam¬ ple, by any device which is capable of reading data in a standard format.

In accordance with a preferred embodiment of the present invention the apparatus for sensing is contained in a hand-held housing. Preferably the apparatus for sensing includes apparatus for communication of the non-individual dependent output.

In accordance with a preferred embodiment of the present invention, the apparatus for communication is operative to communicate information which can be used to reconstruct an individual's hand imaging style.

In accordance with a preferred embodiment of the present invention, the apparatus for sensing does not require a tablet. Alternatively it may include a tablet. Additionally in accordance with a preferred embodiment of the invention, the apparatus for acquiring and encoding communicates via a modem. The communication may be in a fax format or alternatively in a compressed non-raster format. Preferably, the communication is wireless communication.

There is also provided in accordance with a preferred embodiment of the present invention communication apparatus for hand imaging including apparatus for sensing features of hand imaging of an individual which features are highly characteristic of the individual but which also contain information relating to images represented thereby, and apparatus for providing a non- individual dependent output indicating the images in response to the sensed features.

Additionally in accordance with a preferred embodiment of the present invention the apparatus for sensing features is contained in a hand-held housing.

Still further in accordance with a preferred embodiment of the present invention the apparatus for sensing features is contained in a tablet assembly. Further in accordance with a preferred embodiment of the present invention the communication apparatus also includes apparatus for communication of the non-individual dependent output.

Additionally in accordance with a preferred embodiment of the present invention the communication apparatus also in¬ cludes apparatus for communication of the non-individual depend¬ ent output.

Further in accordance with a preferred embodiment of the present invention the communication apparatus also includes apparatus for communication of the non-individual dependent output.

Still further in accordance with a preferred embodiment of the present invention the communication apparatus is operative to communicate information which can be used to reconstruct an individual's hand imaging style.

Additionally in accordance with a preferred embodiment of the present invention the sensing apparatus does not require a tablet.

Further in accordance with a preferred embodiment of the present invention the apparatus for communication includes a modem.

Still further in accordance with a preferred embodiment of the present invention the apparatus for communication is operative to communicate in a fax format.

Additionally in accordance with a preferred embodiment of the present invention the apparatus for communication is operative to communicate in a compressed non-raster format.

Further in accordance with a preferred embodiment of the present invention the apparatus for communication is opera¬ tive for wire communication.

Still further in accordance with a preferred embodi¬ ment of the present invention the apparatus for communication is operative for wireless communication.

Additionally in accordance with a preferred embodiment of the present invention the apparatus for sensing features includes apparatus for sensing the instantaneous angle of motion during hand imaging.

Further in accordance with a preferred embodiment of the present invention the apparatus for providing a non-individ¬ ual dependent output is operative for providing an output indica¬ tion of strokes generated during hand imaging.

There is also provided in accordance with another preferred embodiment of the present invention communication apparatus for hand imaging including apparatus for sensing fea¬ tures of hand imaging of an individual which features are highly characteristic of the individual but which also contain informa¬ tion relating to images represented thereby, and apparatus for providing an output indicating the images in response to the sensed features, and wherein the apparatus for sensing features including apparatus for sensing the instantaneous angle of motion during hand imaging and providing an output indication of strokes generated thereby.

There is also provided in accordance with another preferred embodiment of the present invention apparatus for communicating hand imaging including hand-held apparatus for sensing motion and providing an output in a compressed form which can be transmitted by a conventional modem, LAN or other communi¬ cations medium.

Further in accordance with a preferred embodiment of the present invention the apparatus includes apparatus for re¬ ceiving communicated stroke content information and being opera¬ tive for reconstructing therefrom hand-imaging information.

Still further in accordance with a preferred embodi¬ ment of the present invention the apparatus for receiving is operative to reconstruct hand-imaging information in three dimen¬ sions.

There is also provided in accordance with another preferred embodiment of the present invention communication apparatus for hand imaging including apparatus for sensing motion during hand imaging and providing an output indication of stroke content in a compressed format, and apparatus for receiving communicated stroke content information and being operative to reconstruct therefrom hand-imaging information.

Further in accordance with a preferred embodiment of the present invention the apparatus for receiving is operative to reconstruct hand-imaging information in three dimensions.

There is also provided in accordance with another preferred embodiment of the present invention a communication method for hand imaging including sensing features of hand imaging of an individual which features are highly characteristic of the individual but which also contain information relating to images represented thereby, and providing a non-individual de¬ pendent output indicating the images in response to the sensed features.

There is also provided in accordance with another preferred embodiment of the present invention a communication method for hand imaging including sensing features of hand imag¬ ing of an individual which features are highly characteristic of the individual but which also contain information relating to images represented thereby, and providing an output indicating the images in response to the sensed features, and wherein the sensing features includes sensing the instantaneous angle of motion during hand imaging and providing an output indication of strokes generated thereby.

There is also provided in accordance with another preferred embodiment of the present invention a method for commu¬ nicating hand imaging including hand-held apparatus for sensing motion and providing an output in a compressed form which can be transmitted by a conventional modem, LAN or other communications medium.

There is also provided in accordance with another preferred embodiment of the present invention a communication method for hand imaging including sensing motion during hand imaging and providing an output indication of stroke content in a compressed format, and receiving communicated stroke content information and reconstructing therefrom hand-imaging informa¬ tion. The present invention will be understood and appreciat¬ ed more fully from the following detailed description, taken in conjunction with the drawings in which:

Fig. 1 is a pictorial illustration of a device for acquiring and encoding hand imaging which is constructed and operative in accordance with a preferred embodiment of the present invention in an operative environment;

Fig. 2 is a simplified illustration of a preferred structure of the acquiring and encoding device of Fig. 1;

Fig. 3 is a pictorial illustration of another device for acquiring and encoding hand imaging which is constructed and operative in accordance with a preferred embodiment of the present invention in an operative environment;

Fig. 4 is a simplified illustration of a preferred structure of the acquiring and encoding device of Fig. 3;

Figs. 5A and 5B are pictorial illustrations of a pocket held communicator unit and pen incorporating the apparatus of Figs. 1 and 2;

Fig. 6 is a simplified illustration of apparatus for communicating and reconstructing three-dimensional hand imaging information in accordance with a preferred embodiment of the present invention.

Attached herewith are the following appendices which aid in the understanding and appreciation of the invention shown and described herein:

Appendix A is a detailed exposition of the findings of the inventor concerning the characterization of hand imaging via pen strokes;

Appendix B is a computer listing comprising a preferred implementation of a portion of the programming of microcontroller 26;

Appendix C is an explanation of the computer listing of appendix B;

Appendix D is a computer listing comprising a preferred implementation of a portion of the programming of microcontroller 46;

Appendix E is an explanation of the computer listing of appendix D;

Appendix F is a computer listing comprising a preferred implementation of a portion of the programming of computer 13;

Appendix G is an explanation of the computer listing of appendix F;

Appendix H is a computer listing comprising a preferred implementation of a portion of the programming of computer 13; and

Appendix I is an explanation of the computer listing of appendix H.

It has been found by the present inventor that each writer produces conventional alphanumeric characters as well as graphic images, i.e. hand imaging, from pen strokes selected from a set containing approximately 12 - 14 pen strokes which set is characteristic of each individual. The present invention employs this realization to provide communication apparatus for hand imaging. Appendix A contains a detailed exposition of the finding of the inventor.

Reference is now made to Fig. 1, which illustrates a pen for communicating hand imaging 10 constructed and operative in accordance with a preferred embodiment of the invention in a typical operating environment wherein it communicates by wireless communication with a communicator 11, such as a telephone or RF modem having an associated receiver, such as a model RB 1023 RF receiver, commercially available from RF Monolithics, Inc. of Dallas, Texas. Alternatively, communication may be effected via any other suitable communications medium such as, for example, a local area network and a cellular telephone network.

The hand imaging communicating pen 10, which is illus¬ trated in greater detail in Fig. 2, may be used on any writing surface or alternatively in the absence of a writing surface and does not require any special pad or writing substrate. Preferably the handwriting input device comprises a housing 12 in the gener¬ al size and shape of an ordinary pen.

Communicator 11 may communicate via telephone or coax¬ ial cabling or wireless facilities with any suitable receiver, such as a computer terminal 13 having an associated printer 14. Thus it may be appreciated that the hand imaging produced by the user using apparatus 10 may appear nearly instantaneously on a printed hard copy or a computer screen at a remote location. The same general type of apparatus may be employed for display on a television screen or for output on a facsimile receiver.

As seen in Fig. 2, disposed in housing 12 is an ink reservoir and output point assembly 16, which may be constructed and operative in any conventional manner. Alternatively no ink output may be provided. In accordance with a preferred embodiment of the present invention there is disposed in a forward location of the housing 12 an accelerometer 20, preferably operative in three dimensions. A typical accelerometer which meets the size and power requirements of the invention comprises three mutually orthogonally mounted Model 3031-002 accelerometers commercially available from EuroSensor of 20 - 24 Kirby Street, London, Eng¬ land. Alternatively, more than 3 Model 3031-002 accelerometers may be used.

The output of the accelerometer 20 is supplied via an operational amplifier 24, such as a model LT1179, commercially available from Linear Technology Corporation of Milpitas, Cali¬ fornia, to a microcontroller 26, such as an Hitachi H8/536 micro¬ controller including an A/D converter. Microcontroller 26 is operative to extract a plurality of predetermined features of the acceleration sensed by accelerometer 20. It is a particular feature of the present invention that a relatively small number of discrete features derived from sensed acceleration during hand imaging has been found to be sufficient to map the alphanumeric symbols and graphic output of a given individual. It is appreci¬ ated that the characteristics of such features vary from individ¬ ual to individual and it is often desirable to communicate these personal characteristics. Accordingly, the microcontroller 26 is programmed in order to preserve and communicate not only the information content but also the personal hand imaging character¬ istics of the writer.

A preferred listing in the C programming language of software that provides the functionality of the microcontroller 26 appears in Appendix B. A brief discussion of the principles underlying the functionality of the microcontroller 26 as exem¬ plified in the software of Appendix B, appears in Appendix C.

The microprocessor 26 is operative to extract a plural¬ ity of strokes and to encode them in a conventional universal code, such as ASCII, which is not in any way dependent on the personal handwriting characteristics of a given individual and which can be readily accepted by conventional computers, modems and the like.

Preferably, the coded symbol output from microcontrol¬ ler 26 is in a form compatible with or identical to the output conventionally received from or modem input to a conventional computer, such as a PC.

In accordance with a preferred embodiment of the present invention, the coded output of microcontroller 26 is transmitted to communicator 11 in a wireless manner by a wireless transmitter 32, such as a model MB1003, which is also commercial¬ ly available from RF Monolithics, Inc. and which communicates with receiver 12 (Fig 1) . Alternatively any other suitable IR transmitter or radio transmitter may be utilized.

Alternatively a non-wireless communication connection may be provided as described hereinbelow with reference to Figs. 5A and 5B. In such a case, a non-volatile memory such as a flash RAM 34 is preferably provided to store the output of the micro¬ controller 26. A suitable battery 33 is provided to power the apparatus located within housing 12.

It will be appreciated that the apparatus of the present invention is preferably a hand-held "pen" which can be carried by a user and used with any suitable communication facil¬ ities. The communication facilities and computers as well as peripherals communicating therewith need not be personalized in any way, inasmuch as all of the handwriting recognition hardware and software is resident in the "pen".

Reference is now made to Figs. 3 and 4 which illus¬ trates an alternative embodiment of the present invention which employs a tablet assembly 40 instead of pen 10. The tablet assem¬ bly 40, as seen in Fig. 4, may comprise any conventional graphic input tablet 42, such as a Summagraphics compatible tablet which operates together with a dedicated pen 44 and which outputs x,y coordinates and pen lift signals to a microcontroller 46.

Microcontroller 46 may have all of the relevant func¬ tionality of microcontroller 26 described hereinabove for stroke extraction and encoding and may communicate directly with a modem/fax unit 48, such as is commercially available from Rock¬ well and other suppliers. A preferred listing in the C program¬ ming language of software that provides the functionality of the microcontroller 46 appears in Appendix D. A brief discussion of the principles underlying the functionality of the microcontrol¬ ler 46 as exemplified in the software of Appendix D, appears in Appendix E.

The output of modem/fax unit 48 may be connected to an ordinary telephone or network jack 50 for communication in the manner described hereinabove in connection with Fig. 1.

Appendix F sets forth a software listing in the C programming language for reconstruction functionality of the information communicated by the tablet assembly 40. This software may be resident in computer 13 or alternatively in any output device whose function it is to provide a useful output from the communicated hand imaging information. Appendix G is a brief discussion of the principles underlying reconstruction function¬ ality embodied in the listing of Appendix F.

Appendix H sets forth a software listing in the C programming language for reconstruction functionality of the information communicated by the pen 10. This software may be resident in computer 13 or alternatively in any output device whose function it is to provide a useful output from the communi¬ cated hand imaging information. Appendix I is a brief discussion of the principles underlying reconstruction functionality em¬ bodied in the listing of Appendix H.

Further in accordance with a preferred embodiment of the present invention there is provided portable information storage and retrieval apparatus including a portable computer memory and output device and having as an input element hand-held apparatus of the type described hereinabove.

Reference is now made to Figs. 5A and 5B which illus¬ trate acquisition and encoding apparatus for hand imaging and communication apparatus in accordance with an alternative embodi¬ ment of the present invention. Here a communicator 100 is formed with a socket 101 for removably accepting acquisition and encod¬ ing apparatus 102. Data communication contacts 104 and 106 are disposed respectively at an end of the apparatus 102 and in socket 101 for permitting downloading of written information from apparatus 102 to the communicator 100. Preferably the communica¬ tor is designed to be pocket sized.

Reference is now made to Fig. 6, which illustrates the use of the apparatus of the invention for communicating three dimensional information. A user employing the apparatus outlines the shape of a three dimensional object, such as an airplane model. The user may follow the lines of an existing three dimen¬ sional model, as in the illustration, or alternatively, may draw in a free hand manner. The hand imaging produced by the user is communicated via communicator 11 to a communicator 120 and thence to utilization apparatus such as a CNC machine 122 or a three dimensional model building machine 124, such as is commercially available from Cubital Ltd. of Ramat Hasharon, Israel.

Communication of hand imaging information in accordance with the present invention may be in two different modes. Where compression is desired along the communication channels, the hand imaging information may be transferred in a penstroke language. In such a case, a hand imaging reconstructor is required at the remote location. Alternatively, the hand imaging information can be reconstructed upstream of communicator 11, by suitable recon¬ struction apparatus. In such a case, the hand imaging information may be communicated in conventional CAD format.

It will be appreciated by persons skilled in the art that the present invention is not limited by what has been par¬ ticularly shown and described hereinabove. Rather the scope of the present invention is defined only by the claims which follow:

APPENDIX A

What the human brain tells the human hand A behavioral perspective

Ehud Bar- On

E.B. Research Development Ltd.

Guttwirth Bldgs, Technion. Haifa 32000, Israel

September 22, 1992

Abstract

This study investigates the primitives of motoric patterns of hand movement during handwriting. This is referred to as the "language" between the "hand and the "brain", and as such, has its own vocabu¬ lary and syntax. The "vocabulary" is the pen strokes and the syntax is how they are combined to pen strokes sequences. The handwriting is viewed as a high level cognitive activity of communicating, expressed as a complex motor skill and its investigation provides insight into the processes of chunking and automaticity. The main finding of this study is that pen strokes are specific to an individual writer, and character¬ ize the writer^'s unique motoric control mechanism. The dynamic data of many thousand handwritten characters, produced by many writers, had been analyzed. The time domain signals were segmented into dis¬ crete pen strokes units and represented as vectors in a feature space. Those vectors were clustered, using a variety of clustering techniques. We found that in spite of the fact that the hand movements during writing could take any form or shape, a particular writer employs only a very limited set of pen strokes. The results of the clustering by var¬ ious methods, yields a limited set of only twelve to fourteen types of pen strokes that accounts for ΘObrain supposedly chunks information to minimize the required attentional resources. Keywords: handwriting, human motor control. chunking. auto :.::c- ity. connectionism

1 Humans motor behavior

When we speak about "what the human brain teii≤ the human h d^" , we speak about the kind of ^"'motor control language^", that might exist oetween the "brain^" and the ^"hand" . While this ^''brain-hand^" communication can be approached from different points of view, we study it from a behavioral perspective. That is. we investigate the evidence of chunks or what is referred to as ^"'motor programs" , by analyzing the dynamic data collected during experiments in hand-writing recognition. Although all the results reported in this article consists only on the output of the handwriting process, we try to make the proposed model cognitive and biological plausible. ^•

We'll start with the cognitive plausibility. There is a strong link between cognitive mechanisms and the human motor behavior. Handwriting is the way that humans express their thoughts through the use of a complex motor skill. Rosenbaum [12], presented handwriting as culmination of several in¬ ternal translation process. First, an abstract message or idea is constructed. Then, it is formulated into appropriate linguistic expression, and then trans¬ lated as a series of efferent commands. There is a basic similarity between speech and writing, so we can assume that both share the same underlying mechanism. The phonemes in speech, correspond to pen-strokes in writing, and the morpheme in speech correspond to letters. The higher levels of ab¬ straction (i.e. words, syntax, lexicon, semantics, prosody and discourse), are probably the same.

There is an empirical evidence, that the ability to sequence behavior, whether in the linguistic domain or drawing domain, depends on a central, amodal mechanism [11]. If this is true, then motoric control should be consid¬ ered as obeying the same rules of the linguistic or other sequential cognitive mechanisms. On the other hand, some of the properties that we discover about the primitives of the motor control language, might be generalized to other cognitive activities. In the case of handwriting, the efferent commands are expressed as pen-strokes. It was found by Wright [17], that different pro¬ duction mechanism are probably controlled by the same high-leve. graphic representation. This view of hierarchical structuring, and a ^" virtual^" repre¬ sentation of motor movement is supported by empirical evidence. Humans can write in a consistent style when they write in small letters in their note¬ books, or when writing much bigger characters on a blackboard. Moreover, people can write with a consistent style (same pen-strokes), when using dif¬ ferent effectors like hand and foot.

As in any behavioral research, resulting behavior is influenced both by the general properties of handwriting and by properties which are specific to an individual writer. This means that a considerable part of the variance can be attributed to individual differences. It has been noticed by many researchers that handwriting stvle is so distinctive, that writers can be rec- ognized according to their hand writing. This is also a common knowledge, and therefore signatures are recognized as a unique identifier of specific writer. Some theories even associate personality traits with hand-writing style. As we intend to show in this article, the primitive pattern of writing, are unique for individuals writers.

As anv other cognitive or motor activitv, human motor control a;oes through a process of development that is equivalent to intellectual develop¬ ment. An interesting well known phenomena is that children's drawing (and after that writing), becomes more refined over the course of development. It has been suggested by many researchers, that early drawing behavior corre¬ lates with young children's cognitive abilities. Van sommers [15]. suggested that drawing may be governed by high-level rules, similar to those governing language processing, and that the development in drawing may parallel the development of language. Goodnow and Levine [9] even suggested a: ^"Gram¬ mar for action: sequence and syntax of children^'s copying." They reported several rules for sequencing drawing strokes. Examples of such rules were:" Start at leftmost point" , "Start at top" , "Start with vertical strokes^", ^"draw horizontal lines from left to right" , etc. The evolutionary rationale for such rules could be to simplify motor planning.

The biological plausibility of an hand writing model, involves two parts: The plausibility of the assumed neurological control, and the biomechanical properties of the hand. The preservation of the writing style while using dif¬ ferent muscles and even organs, is one of the most intriguing questions. The automaticity of writing, suggests a chunking mechanism, but this chunking mechanism is probably not in the motoric system of the hand, but somewhere in the upper control levels of the brain. Therefore, whenever we refer to the ^"hand^" , we dc :t metaphorically. I.e. the ^" hand^" represents the efferent mechanism that accomplishes the motoric control. Recently. Alexander et al. [3], raised the question whether the specific concept of a "motor program^"', is an appropriate foundation for the development of biological plausib.e models of how the brain controls movements. While our current knowled e about the cortical and basal ganglia motor areas is still far from allowing a specific model, it can suggest what models are more neurologically plans: ie than others. Fischbach 7] discusses the hnding about ^"face cells^" aπu ^"motor command cells^" as an evidence for abstraction in the brain. In the monkey^'s visual system, ^"face cells" located in the inferior temporal sulcus. were sug¬ gested as representing a high level of abstraction. These neurons respond to faces but not to other visual stimuli. Face cells have their counterparts on the motor side. " Command^"' neurons have been identified in certain vertebrates that trigger fixed action patterns. Georgopolos [1] recorded electrical activ¬ ity of single neurons, and found command neurons in the monkey^"; motor cortex (precentral gyms) that encode the direction of forelimb movement. The firing of these neurons was not associated with the contraction ;f a par¬ ticular muscle or with the force of the coordinate movement. Geor opoulos computed a vector by summing the firing frequencies of many neurons, and found that it is more correlated with the direction of movement than is the activity of any individual cell. The vector becomes evident several millisec¬ onds before the arm moves. He interpreted this result as evidence for motor neuron planing. Damasio and Damasio [4] discussed the linguistic behavior of patients with lesion in the left posterior temporal and inferior par.etal cor¬ tex. It was found that such patients have problem in producing word forms from the available phonemes. Analyzing the accumulated empirical finding on language structures, gathered with assistance of imaging techniques like RI (Magnetic Resonance Imaging] and PET (Positron Emmision Tomog¬ raphy), shows that linguistic activity like naming, involves the motor cortex activation together with anterior and posterior language centers in the left hemisphere. Writing is a language activity which involves a production cen¬ ter that forms words and activates the "command cells" in the motor cortex to produce pen-strokes sequences (letters) and written words. In the same *vay that speech is composed of a small set of phonemes, we argue that handwritten letters are composed of a small set of pen-strokes.

In addition to the neurological plausibility, there are biomechanic con¬ straints on the "hand^" part. Some general principles have been suggested as governing this control mechanism. For example. Flash and Eogan [8], proposed that humans tend to write in a way that minimizes jerk. That is. the third time derivative of the position signal. A more recent study [6] , suggested the snap, which is the fourth derivative i position, as the cost function that is minimized. As we will see, there are alternative hypotheses about the type of constraints imposed on the biomechanics of handwriting. In spite of the fact that the principle that governs the handwriting might be universal, each writer has its own unique variation. The differences are more pronounced in the unwritten strokes (the pen movements that do not touch the writing surfaceO than in the written ones. The friction of the pen with the writing surface diminishes the characteristics of the hand control mechanism which are better revealed when the pen is up.

The article starts with reviewing theories of ^"'motor programs^" , and ar¬ guing against that term and what it implies. We^'ll propose an alternative connectionist model of primitive hand-writing patterns and argue that it is more biologically and cognitive plausible. Then, we describe the experiment, and the collection of the data. The fourth chapter will describe the analysis of the collected data, and the conclusions that were drawn from this analy¬ sis. The last chapter will discuss the results, and compare our conclusions to alternative ones. We shall conclude the article by pointing out some future directions and implications of the suggested model.

2 Attention, chunking and "motor programs"

The concept of "working memory^" is modeled after the "working memory" in a (von Neumann) computer, where the registers in the Central Processing Unit (CPU) have a similar function. This is also why researchers in that field prefer to talk about "motor programs" . "Motor programs" are supposed to save attentionai resources. According to this approach, it is assumed that the brain controls movements like handwriting, by executing ^"motor- programs^" , much like software is used in a computer (e.g. [12] ). The ^"motor program" concept is attractive, as it reduces the complexity of the sequential, analytical approach by using pre-programmed sequences of a limited number of generic motor commands (or routines), to control a large repertoire of movements. Alexander et al. [3] points out difficulties with the neurological plausibility of "motor programs" that imply separation between ^"software" and ^"hardware^" . For example, what would constitute the software in such a model, and where it is stored when not executed, how are they assembled prior to their execution and how new programs are created. A major problem with the ^"motor program" approach is also the sequencing of performance: goal- d ected movements are supposed to be translated into trajectories, then to joints kinematics. Muscle activation cannot be computed until the inverse dynamics is calculated and so on. Therefore, argue Alexander et al.. signs of specialization for such transformations should have been found in the cortical and basal ganglia. Thusfar. neurobiological evidence seems to indicate lack of such specialization.

It is assumed that the processing capacity is limited, and therefore sev¬ eral tasks that have to be carried out simultaneously compete on the same resources. The main "problem" of human-beings and other organism might be. that we lack a ^"parallel output channel" . All the output channels, be it speech, handwriting or any other motor output, are all serial in nature. It might be that this serial output suggested a serial cognitive mechanism as well. It is the conjecture of the neural-nets literature, and of this article, that the underlying mechanism is parallel and distributed over millions of simple processing units (neurons). Therefore, the term "motor programs^" , that im¬ plies a serial symbolic process running in the "brain-computer^" might be misleading. We prefer to speak about "motoric schemas" . which are motoric patterns invoked by activation of an assembly of neurons.

The connectionist view of schemas (Smolensky. 19S6) is that stored knowledge- atoms are dynamically assembled at the time of inference, into context- sensitive schemata. Rumelhart and McClelland (19S6) [14] proposed a tech¬ nique that suggests how an attentional selective mechanism might work. They propose the use of a set of mapping units which produce ^"dynami¬ cally programmable connections" and achieve focusing on different features on different times. Smolensky ( 19S6) maintains that schemata are coherent assemblies of knowledge atoms, where coherence or consistency is formalized under the name of harmony. He proposes the harmony principle: the cogni¬ tive system is activating coherent assemblies of atoms, and draws inferences that are consistent with the knowledge represented by the activated atoms.

In much the same way, we propose to speak about "motoric schemas" . This is consistent with our conjecture that there is no essential difference between the so-called "cognitive" and "motoric" brain mechanism. The con¬ nectionist schema-model is also consistent with the neural evidence, that the specialization among different cortical motor areas are related to certain sequences of movements, and not to transformations as proposed by the "mo¬ tor program" literature. According to our conjecture, preparatory units and movement executing units will belong to the same schema. This is supported by the anatomical fact that the three motor areas (SMA - Supplementary Motor Area, PMC - Primary Motor Cortex and Putamen), has the same proportion of targe" dependent motor cells and limb dependent movement- related cells ( [2] ). Another supporting evidence is that neuronal population that were supposed to represent different stages of computation ' according to the "motor program^" view), have been shown to be active simultaneously.

3 The experiment 3.1 Data collection

Rumelhart [13] developed a system which learns to recognize cursive script as it is generated by a writer. This system learns from examples of cursive script produced by a number of writers and recorded. He collected approximately 1000 words from each of δS writers. The average length of a word is about 8 characters. That sums up to nearly 500,000 examples of handwritten cursive characters. His results were encouraging and had been used in this research. While Rumelhart [13] was mainly interested in handwriting recognition, this article uses the same data to investigate the writing mechanism.

The data were collected in the following manner. Each word in the corpus was recorded. It was then played to the writer who was instructed to write the word on a tablet digitizer. The resulting x coordinate, y coordinate and an indication of whether the pen was or was not on the paper were sampled each 10 milliseconds. The resolution ( more than 200 dpi) and the sampling rate (100 samples/ ec) are those that are shown to be appropriate in the on-line hand- writing recognition literature ([16] ). The data was saved as files, and has been used for the analysis reported in this article.

In addition to the data from Rumelhart's experiment, several thousands pen strokes of Japanese handwriting were collected. Most of the data has been collected from hand written Hiragana characters, but some data has been collected during writing Kanji (idiographic) Japanese characters. Hi¬ ragana characters has the curved shapes of english hand printed characters. but without the ligature of cursive handwriting.

Preprocessing of the hand-writing raw data has been made, with the goal of extracting features that will be used to segment and characterize the "pen-strokes" . A pen stroke was defined as a segments of the cursive writing signal, between two consecutive zero crossing of the vertical velocity of the pen movement. Each character was segmented to several segments or "pen- strokes" . A typical writing rate in English is two letters per second. Writing Japanese characters (Hiragana). takes about the same time, and a typical Hiragana character can be written in 0.3 - 0.5 seconds.

3.2 Segmentation and feature extraction

The principle of segmentation and feature extraction is to segment the con¬ tinuous signals into discrete segments and to represent each segment by a feature vector in the feature space.

The segmentation that produces "pen-strokes" out of the continuous sig¬ nals, depends on the different definitions of the term" pen-strokes^". While most of the literature about on-line character recognition is using this term, there isn't an agreed upon definition of a "pen-stroke" . For example, one often finds only the pen-state change as the only criteria. That is. defini¬ tion of a stroke as continuous pen-movement, between pen-down and pen-up consecutive states [16].

Once a ^" pen-stroke" is defined, there are many ways to represent it in a feature space. The on-line character recognition research employs several orthogonal transformations such as a discrete Fourier transform of the curve segments corresponding to the pen-strokes. That is. a pen-stroke is repre¬ sented by its Fourier coefficients obtained from its x(t) and y{t) signals. Es¬ sentially, any orthogonal transformation (e.g. Walsh transform, Karhunen- Loeve) could do in approximating the pen-strokes curves. That is. Plane curves can be approximated by orthogonal functions (Sinusoidal, polynomial or even square waves). This description can be also easily converted to the frequency domain, as was done in several studies of hand- writing recognition [16].

This mapping of the time domain to a parametric domain is advanta¬ geous when the characters can be represented by a small number of coeffi¬ cients. Therefore, periodical smooth curves lend themselves better to mod¬ eling bv harmonic functions, as one needs less coefficients. On the other hand, straight line strokes require high order harmonies as they include hi°di frequency components. This is why sinusoidal approximation is useful for characters consisting of curved strokes, as found in English cursive script, more than for Japanese Kanji characters (that are made mainly i straight line segments). K-L expansion has been proved to be a successful algorithm in machine-printed Chinese character recognition. Another successful at¬ tempt was to use a modified Hough transform for recognition o: Chinese hand-written characters. The Hough transform is a technique for line detec¬ tion and has been generalized to detect arbitrary shapes. Chinese characters are line-like, and therefor lend themselves naturally to a Hough transform representation.

The segmentation and feature extraction methods depend of course, on the goal. If the goal is pattern recognition, then the segmentation and feature extraction are geared toward discrimination between the various patterns. In our case, we looked for a segmentation and features that are biological plau¬ sible. Consequently, we investigated only features that might be explained by the neurobiological control structures, like the direction of the strokes, their curvature etc.

3.3 Hollerbach's model

The segmentation and feature extraction mechanism employed was. to de¬ velop a model of the underlying handwriting process and to describe the data in terms of the parameters of the model. The model employed was derived from that of Hollerbach [10] and involved the assumption that the genera¬ tion process could be described as pair of coupled oscillators. The coupled harmonic oscillators is just one of the many models that exist. Actually, its basic assumption about the symmetric shape of the velocity profile (an half sinus shape), is probably an oversimplification. The literature about velocity profiles of pen-strokes usually assumes an asymetrical beii-shaped velocity profile. That is, a rapid-aimed movement described by a log-normal velocity profile is considered as the fundamental unit (stroke). More com¬ plex movements are described in terms of superimposed log-normal curves. The asymmetric nature of the velocity bell-shaped profile results from the global stochastic behavior of a large number of processes involved in velocity control.

In spite of being an oversimplified and inaccurate model, it has a clear advantage that it :s based on a control mechanism, and is neuorobiclogically interpretable. This model assumed that:

y = b cos( _yt ) (2)

In words, the idea is simply that writing involves two orthogonal pendular movements. If we speak about writing in a notebook small size letters), we can think about the wrist horizontal movements (actually, it is more arc-like movements) and the fingers flexion andextension vertical movement. These two movements can be considered as independent. If the size of the letters is more than an intch. than the arm muscles are involved.

According to this model. The (/-axis consists of a series of up/down strokes whose velocity pro .ie is assumed to be sinusoidal. The -axis is also pendular with a constant velocity, c, to the right. Different characters are made by modulating the relative amplitudes, a and 6, the relative phase, phi. and the relative frequency _^■- and i_y , in the x and y directions. It is, furthermore assumed that the parameters change only when the velocity in the y direction reaches zero (end of pen-stroke). Thus, we define a stroke as the motion between zero crossings in the y velocity - v_y. In addition, segmentation occurs when the pen-state changes (from pen-down to pen-up or vice versa).

It should be stressed that Hollerbach's model was designed for synthesiz¬ ing handwritten-like character, bv a second order mechanical svste . This model does not try to imitate the human motor control, or to be used for analysis of human handwriting. However, as it is a control system model, some of the parameters might be interpreted in terms of the human biome- chanical system. For example, the parameter φ, which designates the phase shift, can be interpreted as relating to the delay in the nervous-muscular control system. As such, it can have an important diagnostic value in motor diseases.

When it was applied by Rumelhart to handwriting analysis, it suffered from some drawbacks. One of them is that the model is fitted not to the image, but to the velocity profile of the stroke. This simplifications tend to work well in most of the cases of English cursive hand-writing, because of the periodical nature of the v_y velocity signal.

As the Hollerbach model that we used, is based mainly on the velocity sig¬ nals, we will illustrate the transformation from the x — y domain of tne hand-

Figure tter d

Figure 2: The v_x graph for the handwritten letter d

written character, to the corresponding velocities. Examples of the handwrit¬ ten letter d, the corresponding velocity profiles and the reconstructed d are shown in Figures 1,2,3 and 4.

As can be seen, the reconstruction isn't perfect, and the curvature of the first pen-stroke of the d is opposite to the original. This result illustrates the fact that the model tries to reconstruct the velocities and not the resulting pen strokes image. ,

This does not exclude the fact that sinusoidal approximation worked for Rumelhart in recognition of cursive script. It turned out that in some cases (periodic signals during cursive handwriting in English) the model worked satisfactorily.

The Kanji characters, on the other hand, have more short straight seg¬ ments, as can be seen in the following figures:

The "mori" Kanji character in the picture, is segmented to 27 pen-strokes (the last two pen-down strokes in the third "tree" are missing). Sixteen out of the twenty seven, are strokes in which the pen touched the paper, and 11 were just for moving the pen from one line to the other. Twelve sequences of "pen-down" strokes, correspond to the visible line segments in the character.

Figure 3: The υ_y graph for the handwritten letter d

Figure 4: The reconstructed letter d

Figure 5: The separate strokes are more evident in Japanese Kanji characters. This is the Kanji character: "mori^" . which means: forest

Figure 6: v_y "mori"

Figure 7: The reconstructed Kanji character: "mori".

The reconstructed Kanji character is depicted in the figure.

3.4 Recognizing pen-strokes sequences

One of the key problems in recognizing cursive handwriting is the segmen¬ tation problem. Rumelhart [13] has devised a learning algorithm for cursive handwriting recognition which combines word recognition and letter recog¬ nition. The letter recognition is based on recognizing PMPs, and PMPs sequences make letters. This system involves simultaneously learning to rec¬ ognize and segment letters.

Although Rumelhart's experiment was done for hand-writing recognition, there are several things that can be learned from it. concerning the PMPs and their sequencing during handwriting. It was recognized in the early sixties ([6]). that motor knowledge can be used in recognition of hand-writing. A system called Analysis by synthesis suggests that characters are recognized by their "motor programs" . These "motor programs" are supposedly deduced by guessing an initial program, and iteratively updating it according to the difference between the synthesized and actual forms. The connection between reading and writing process have been corroborated by the co-occurrence of certain ^'kinds of acquired dysgraphia and dyslexia ([5]). In contrast to ear¬ lier approaches, Edelman et al.. ([6]). assumes that while readers use motor knowledge in reading, they do not seem to do so by mentally reproducing the process of writing. The connectionist model that we propose isn^"t both¬ ered, of course, by those distinctions between explicit simulation or implicit knowledge. This is another example of the misleading influence created by the ^"motor programs" metaphor.

4 The results of clustering

4.1 fixed radius clustering

The basic units of clustering were the pen-strokes, each of which was rep¬ resented as a point in an n dimensional space. Out of the six features that we extracted for each stroke only three have been used. First, we used only one frequency for the modeling, so the rare strokes that involved .higher har¬ monies were removed. Second, we did not differentiated between Up-strokes and Down-strokes. Up strokes contain more high order harmonies, but we limited our analysis to the basic movements, and tried to ignore the fluctu¬ ation induced by the bio-mechanical control mechanism. The third feature that wasn't used was the mid-point. For the reconstruction of the pen-strokes in the spatial domain, the x-coordinate of the midpoint in each stroke was computed. However, our preliminary analysis showed that this variable was very highly correlated with the x variable. This preliminary analysis, yielded three variables that were almost uncorrelated: Δ_r, Δy and velocity. The dimension of the space were:

1. Δ_v - The relative displacement on the vertical direction.

2. Δ - The relative displacement on the horizontal direction. 3. v - v The v_: velocity at the end of the stroke is calculated according to equation below.

For a pen-stroke between a and b. which is approximated by a certain oscillation frequency, we calculate:

v = [~ ^~] ^~ [ ] (4)

(5)

It is calculated in a different way for different a;t and this is an example of such a calculation.

The clustering of data from many writers, didn^'t yield satisfactorily clus¬ tering, but the clustering of individual writers did. E.g. the clustering for a particular writer, revealed 13 compact clusters that contained 90to 14. were consistent to all the writers we analyzed. This is a corroboration to our con¬ jecture that hand writing is made out of a small number of PMPs. which are unique to an individual writer.

The centroids of the clusters, were reconstructed from the feature space, and are displayed on a 2-D spatial domain. As can be seen clearly from the results, different writers have different stroke types:

As each writer has about 25.000 pen-strokes that we wanted to cluster, we started with a fast clustering algorithm, similar to the k-means algorithm. The main requirement of the clustering algorithm were that it will be able to deal with very large data sets and find satisfactory clusters in few (2-3) iterations. The other requirement, which was even more important, was that the centroids will be good representations of the observations within each cluster. This requirement lead to seeking compact, hyperspherical clusters, that do not exceed a predefined radius. Elongate clusters are therefore repre¬ sented by several adjacent clusters. Those clusters will be merged in a latter stage by an hierarchical clustering algorithm.

The clustering employed a two phase strategy. First, a fast ^"nearest centroid sorting^" algorithm was employed to reveal the clusters in the large data set. Then, the resulting centroids of the clusters have been submitted to different hierarchical clustering methods. The first phase algorithm was

Figure S: Clustering of 25,000 strokes of the same writer. Gray clusters represent down strokes.

Figure 9: Thirteen centroid pen-strokes of ah individual writer, including their relative frequencies. sensitive to outlier strokes, that formed separate clusters. This was the reason why we got many very small clusters. These clusters accounted for less than lOof the observations. They were considered to be noise, or very exeptional pen strokes, and have been removed so not to influence the representativeness of the centroids of the large clusters.

The second phase included clustering of the resulting centroids using ten different methods. We distinguished between methods that yield compact hyperspherical clusters, and those that can detect elongate clusters. We start with the first group of eight clustering methods:

1. Average Linkage cluster analysis

2. Centroid hierarchical cluster analysis

3. complete linkage cluster analysis

4. Equal variance maximum likelihood method

5. Flexible data cluster analysis

6. McQuitty's similarity analysis

7. Median Hierarchical cluster analysis

S. Ward's minimum variance cluster analysis

The different methods tend to favor different characteristics such as size, shape or dispersion. For example, methods based on the least-squares cri¬ terion such as k-means or Ward^'s minimum variance method, tend to find clusters with roughly the same number of observations in each cluster. Aver¬ age linkage is biased toward finding clusters of equal variance. Most cluster¬ ing algorithms, except for single-linkage and density-linkage, tend to produce compact, roughly hyperspherical clusters. The clustering methods which are based on nonparametric density estimation, like the single linkage, will be discussed later in this chapter.

All the above clustering methods yielded very similar results, and the tree-based partition was essentially the same. The use of many different algorithms has been employed to investigate the robustness of the clustering structure under different hierarchical clustering methods. The result of all

Figure 10: Hierarchical (compact) clustering of the 12 pen-strokes centroids of a particular writer

the above method revealed the following tree-based partition of the set of the basic twelve pen-strokes (of a particular writer).

From looking at the results of the hierarchical clustering, there is an ob¬ vious super-clusters that emerge. The horizontal-left strokes are one such a group, long down strokes are another group. In general we see a distinc¬ tion between horizontal strokes and vertical strokes. The horizontal strokes themselves are subdivided to horizontal-left directed strokes, and horizontal right and up directed strokes. The high velocity C shaped strokes are part of circles or ovals. It should be noticed that for a specific writer, a certain stroke is always accomplished in the same way. For example, an horizontal short stroke, like crossing a t, will be done always as left directed strokes. Someone else could use only horizontal right directed strokes for that pur¬ pose. However, it is very unlikely that the same writer will use both an horizontal-left and horizontal-right strokes. The same is true with long ver¬ tical strokes. Once the writer is using a long vertical down-stroke, he will produce vertical lines always as down strokes of the same type and velocity profile. This organization of pen strokes was consistent in all the hierarchical

Figure 11 : Hierarchical (Density linkage) clustering of the 12 pen-strokes of the same writer

clustering algorithms that we mentioned above.

The clustering methods that employ nonparametric density estimation, like the "Density linkage cluster analysis" , can detect also elongated cluster shapes. These clustering techniques yielded two distinct super clusters: the "down and long pen-strokes" , and the "up and right strokes". The down strokes are those that form the "back-bone" of the English characters, while the up-right strokes are typically those that are used as ligature.

4.1.1 The "characteristic" shape of pen-strokes

As was argued above, any writer has a specific set of pen strokes that char¬ acterize the writer. While the same writer will have similar pen-strokes, in writing different languages, the frequency of appearance of a specific pen strokes depends, of course, on the language. In order to characterized a spe¬ cific writer, in respect to her/his pen strokes, we suggest the "Pen-strokes Ordering Diagram^" (POD). Such PODs are displayed in the following figures. In spite of their strange looking, those diagrams are quite valuable, and convey important information about the handwriting of the analyzed writer.

r y J

Figure 12: The centroids of the pen strokes of a writer, for English cursive writing. The pen-strokes are ordered according to their v_y values, from up¬ strokes to down strokes

Figure 13: The centroids of the pen strokes of a Japanese writer, for Japanese Hiragana characters. The pen-strokes are ordered according to their v_y val¬ ues, from up-strokes to down strokes

Figure 14: The centroids of the pen strokes of a Japanese writer, for english characters. The pen-strokes are ordered according to their v_y values, from up-strokes to down strokes

There is a very clear distinction between the key strokes that these two writers are using. This is true to other writers as well. Each writer uses a unique set of pen-strokes: different slopes, different curvetures. different velocity profiles and accelerations.

5 Discussion and future research

We will start our discussion with comparing the conclusions of Rumelhart 's handwriting recognition experiment, and the conclusions of this study. In Rumelhart's handwriting recognition experiments, both writer dependent and writer independent recognizers have been trained. Two networks have been trained to recognize the writing of individual writers and one network has been trained on four different writers as a "writer independent^" recog¬ nizer. On the writer dependent networks Rumelhart found that, for a vo¬ cabulary of 1000 words, on words never seen during training that 99top five, approximately 90On the writer independent data the results are somewhat worse. That is. about 70

According to the results in this study, we have a basis to doubt this conclusion. The inter-writer variability is too big. and more writers will not lead necessarily to better results.

Another finding of Rumelhart was that writers can be trained easily to produce recognizable hand writing. He developed an ^"online^" system in which the network recognizes (and can be trained) as the writer writes on the digitizer. With a little care on the part of the writer it is not difficult to achieve a score of better than 90coιτectly classified on the writer independent system. ( It is also possible to write so that the recognizer does much more poorly than that. Careful experiments on a person^'s ability to adjust to the recognizer have not been carried out. By limiting the vocabulary to one hundred words or less, it seems to be possible to obtain near perfect performance. (It will, of course, depend on the confusability of the words.)

The main conclusion was. that it would be useful to embed the recognizer in a network of networks each trained on a subset of the writers - perhaps one for printers, one for pure cursive writers etc. This line of thought led to the current study, reported in this article. That is, that it may be useful to study the individual differences among the writers. The idea of studying individual differences, as a mean towards better handwriting recognition, turned out to start a new line of research - the study of writer's unique pen-strokes, which is related to the topic of automaticity in brain - hand communication.

This study started from that point. The main question that we posed was if individual writers have distinct sets of pen-strokes, which are con¬ sistent and well defined. The reanalysis of the data from this perspective encourage to believe that this is the case. Human writers have 12-14 distinct pen-strokes, which are characteristic for a certain writer. These pen-strokes are the primitive ^"motoric patterns", of which handwriting is composed. We showed also that the primitive pen-strokes cluster to super-clusters, thus re¬ vealing the hierarchical nature of the control mechamnism. These findings are consistent with the neurological literature, that we cited in the introduc¬ tion. That is. there might be "command cells" , that get the activation for certain words (letter combinations) from another center in the brain and acti¬ vate pen-strokes mechanism. The pen-stroke is controlled by a direction and amplitude cells, that activate the corresponding primitive motoric patterns (PMPs). The next stage will be to locate the cell regions that are responsi¬ ble for this activation in the motor areas of the human brain. This is under research now with the help of Magnetic Resonance Imaging (MRI , method, when the MRI is tuned to detect cerebral blood flow. We would expect that the learning to write, should show itself as forming of such motoric activation centers, corresponding to what we have found in this study.

Those findings have implications to the study of automaticity a c chunk- ing. One question to be investigated is if the motor control mecnanism is central and a modal, as suggested by previous researchers. This can be in¬ vestigated by studying the patterns of interference between modalities. For example, an experiment in which the subject is instructed to pronounce one character, and write another character at the same time. In addition to pre¬ dicting longer Reaction Time, we can now predict interference between the pen stroke patterns and sequences. Another interesting question is how are the motoric patterns stored and how are they retrieved when needed. Our conjecture, which is consistent with the neural net model, is that the retrieval time will be independent of the number of patterns sequences. Some sup¬ port for this conjecture is that it takes the same time to write a character in a large character set (kanji) or small character set language (Hiragana. English).

Future research that will combine behavioral analysis with neurobiolog- ical research, might answer ma " of the questions that we raised in the introduction.

References

[1] R. E. Kettner A. P. Georgopoulos and A. B. Schwartz. Primate motor cortex and free arm movement to visual targets in 3-d space. Journal of Neuroscience. pages 292S-2937. 1988.

[2] G. E. Alexander and M. D. Crutcher. Neural representations of the target of visually guided arm movements in three motor areas of the monkey. Journal of Neurophysiology, 64:164 - 178. 1990.

[3] G. E. Alexander, M. R. De-Long, and M. D. Crutcher. Do cortical and basal motor areas use motor programs to control movement ? Behavioral and Brain Sciences. 1992.

[4! A. R. Damasio and H. Damasio. Brain and language. Scientific Ameri- ^' can. 267(3):SS - 95, 1992.

[5ι S. Edelman. Reading and writing cursive script: A computational study. PhD thesis. Weizmann Inst. of Science, Rehovot. Israel. 1988. [61 S. Edelman. T. Flash, and S. Ullman. Reading cursive handwriting by alignment of letter prototypes. International journal of Computer Vision. 5:303-305, 1990.

[7] G. D. Fischbach. Mind and brain. Scientific American. 267 3 :4S-57. 1992.

[8^' T. Flash and X. Hogan. The coordination of arm movements: an ex¬ perimentally confirmed mathematical model, journal of Neuroscience. 5:1688-1703. 1985.

[9] J. J. Goodnow and R. Levine. The grammar of action: Sequence and syntax in children's copying. Cognitive Psychology, pages 82-9S. 1973.

[10] J. M. Hollerbach. An oscillation theory of handwriting. Biological Cy¬ bernetics. 39:139-156, 1981.

[11] S. W. Keele. Sequencing and timing in skilledv perception and action: An overview. In D. Allport. W. Mac Kay, W. Prinz, and E. Sheerer, editors, Language perception and production. Academic Press. London. 1987.

[12] D. A. Rosenbaum. Human motor behavior. Academic Press, 1991.

[13] D. E. Rumelhart. Segmenting and recognizing online cursive handwrit¬ ing. Technical report. PDP Lab., Stanford University. 1992.

[14] David E. Rumelhart. G. E. Hinton. and R. J. Williams. Learning internal representations by error propagation. In D. E. Rumelhart and J. L. McClelland, editors. Parallel Distributed Processing, chapter S. pages 31S-362. MIT Press, 1986.

[15] P. Van Sommers. Drawing and cognition: Descriptive and experimental studies of graphic production processes. Cambridge Univ. Press. Cam¬ bridge, England. 19S4.

[161 C C Tappert. C. Y. Suen. and T. Wakahara. The state of the art in on-line hand writting recognition. IEEE Trans, on Pattern Analysts and Machine Intelligence. 12(8). 1990. [17] C. E. Wright. Generalized motor programs: Reexamining claims of effector independence in writing. In M. Jeannerod. editor, Attention and performance XII pages 294-320. Lawrence Erlbaum, Hillsdaie, N.J., 1990.

#include <conio.h> #include <stdio.h> #include <signal.h> #include <math.h> #include <stdlib.h> #include <graphics.h> #include <dos.h> #include <string.h> #include <values.h> #include <iostream.h> #include <alloc.h> #include <time.h> #include <ctype.h> #include "qjib.h" #include "start.h"

#define DMAX 400

#define PERIOD_CONST 10

#define T 0.01

#define NUMBER.CHANNEL 5

#define del 20 pos_st POS_ST[DMAX];

FILE *pointJfp; int pen_up=0; int init_board (void); extern unsigned _stklen=0x8000; int read_acc (char ^*filename); void ini_msg(void); void next_point(void); void read_smooth_point(int number); void read_averag_point(float xyz[],long num); void get_acc(void);

#ifdef cplusplus typedef void (^*fptr)(int); #else typedef void (*fptr)(); #endif unsigned int shift_cnt=0; int Catcher(int *reglist)

{ printfC'Caught it!\n"); /^* make return AX = 3 ^*/ return 0; } /* First filteration by Butterworth digital filter 4'th order and

0.1 cutoff freq.

Input : array of accel. POS_ST and index of point indl .

Output : smoothed values in array POS_ST */ void butter_filt4(pos_st POS_ST[],int indl)

{ static float a[5]={1. ,-3.180639,3.861194,-2.112155,-438265}; static float b[5]={.000416599,.001666397,.002499595,.001666397,.000416599}; static float y[4][5],x[4][5]; int i,j; float sum=0.; static int cnt=0; for(j=0;j<4;j++)

{ if ( cnt<5 ) yp][4-cnt] = POS_ST[ind1].AR[j]; for(i=4;i>0;i--) ^χϋ]D] = ^χD][i-i]; xO][0] = POS_ST[ind1].AR[j]; if ( cnt >= 5 )

{ sum = 0; for(i=4;i>0;i--) yϋ][i] = yϋ][i-i]; for(i=0;i<5;i++) sum += b[i]^*x[j][i]; for(i=1 ;i<5;i++) sum -= a[i]^*y[j][i]; y[j][0] = sum;

POS_ST[ind1].AR[j] = sum; } } if ( cnt < 5 ) cnt++;

}

float DIF[2]={0,0},DIF0[2]={0,0};

/* Second filteration by Butterworth digital filter 4'th order and

0.02 cutoff freq.

Input : X and Y accel.

Output : smoothed values in global variable DIF ^*/ void butter_filtO(float dif[2],int fl)

{ s t a t i c d o u b l e a[5]={1.e+00,-3.83582554064735e+00,5.52081913662223e+00,-3.53353521946302 e+00,8.48555999266478e-01 }; s t a t i c d o u b l e b[5]={8.98486146372335e-07,3.59394458504525e-06, 5.39091688001037e-06,3.59 394458371298e-06, 8.98486146816424e-07}; static float y[2][5],x[2][5]; int i,j; float sum=0.; static int cnt=0; if (cnt == 5 && fl) cnt=0; for(j=0;j<2;j++)

{ if ( cnt<5 )

{ for(i=0;i<5;i++)

{ yfflli] = dlfffl; xfflli] = difUl; } } for(i=4;i>0;i-) xffl[i] = xffl[i-1]; xϋ][0] = difϋ]; if ( cnt >= 5 )

{ sum = 0; for(i=4;i>0;i-) yD][i] = yD][i-U; for(i=0;i<5;i++) sum += b[i]^*x[j][i]; for(i=1;i<5;i++) sum -= a[i]*y[j][i]; y[j][0] = sum; DIF[j]= sum; } } if ( cnt < 5 ) cnt=5;

} /* Function for compensation of second filter delay, make delay for outputs of first filter. Input:d1 and d2 current difference of signals in a pair Output: delayed difference signal ^*/ void set_delay(float d1 , float d2, float &res1 .float &res2) {static h_cnt=0,t_cnt=0; static float diff_acc[2][50]; diff_acc[0][t_cnt]=d1 ; diff_acc[1 ][t_cnt]=d2; if ( (h_cnt-t_cnt+del)%del == del-1 )

{ resl = diff_acc[0][h_cnt]; res2 = diff_acc[1 ][h_cntj; h_cnt = (h_cnt+1 ) % del;

} Lent = (t_cnt+1 ) % del;

} struct send_data {int period; 111 float ampl; //A float incr_acc; IN

}; send_data params[2][DMAX];

/^* This procedure gets as inputs two signals (after the filteration) for each accelerometer and the pen status. The procedure performs segmentation of the signals and calculation of each segment's features */ void get_pack_param(send_data params[2][DMAX]) {static int fl_opt=0,cnt_seg[2]={0,0}; static int cnt_t[2]; static float p_dacc[2]={0,0},beg_dacc[2]={0,0}; static float max_ampl[2]={0,0},beg_xyacc[2]={0,0}; int i; float dacc[2]; set_delay(POS_ST[1 ].AR[0]-PEN.R[0]-POS_ST[1 ].AR[1] + PEN.R[1 ], POS_ST[1].AR[2]-PEN.R[2]-POS_ST[1].AR[3] + PEN.R[3], dacc[0],dacc[1]); if (!pen_up)

{ for (i=0;i<2;i++)

{ if ( fLopt )

{ if ( dacc[i]^*p_dacc[i] <= 0. )

{ params[i][cnt_seg[i]].period=cnt_t[i]; params[i][cnt_seg[i]].ampl=max_ampl[i]; params[i][cnt_seg[i]].incr_acc=DIF[i]-beg_xyacc[i]; cnt_seg[i]++; params[i][cnt_seg[i]].period= -1 ; p_dacc[i]=dacc[i]; beg_dacc[i]=dacc[i]; beg_xyacc[i]= D I F[i] ; max_ampl[i]=0; cnt_t[i]=0;

} else

{ p_dacc[i]=dacc[i]; if (fabs(max_ampl[i])<fabs(dacc[i])) max_ampl[i] = dacc[i]; cnt_t[i]++; } } if (!fl_opt && (fabs(dacc[0])>5. II fabs(dacc[1])>5.))

{ f Lopt = 1 ; cnt_t[0]= cnt_t[1] = 0; memcpy(p_dacc,dacc,sizeof(float)^*2); memcpy(beg_dacc,dacc,sizeof(float)*2); memcpy(beg_xyacc,DIF,sizeof(float)^*2); } } } else

{ params[0][++cnt_seg[0]]. period= -1 ; params[1 ][++cnt_seg[1 ]].period= -1 ; }

APPENDIX C

The microcontroller gets as inputs the signals of the accelerometers, performs segmentation of it in time (listing of the segmentation program is in appendix B) and represent each segment by several parameters which are transmitted to the receiver. The segmentation of each signal is performed by the movement of the center of oscillations and amplitude and frequency of the oscillations. The procedure of segmentation and feature's extraction consists of the next steps:

Dividing of the original signal into two components: the signal of the movement of the center of oscillations and the signal of oscillations. The first signal is obtained by filtering the acceleration signal from an accelerometer using Butterworth digital filter as described in Digital Filter Design, T.W.Parks and C.S. Burrus, John Wiley & Sons, 1987, chapter 7, section 7.3.3, with 4'th order and 0.02 cutoff frequency. The second signal is obtained by filtering of the differential signal of two accelerometers in a pair, using the Butterworth filter with 4'th order and 0.1 cutoff frequency.

Segmentation of the signal according to the zeros of the oscillations signal. Features extraction - The features that we are using to define each segment are:

Duration between the edges of the segment (T).

Amplitude of oscillations (A).

The difference between the values of first signal at the edges of segment (V)

Fax Pen

Generator of strokes for hand imaging

#include <εtdio.h>

#include <alloc.h>

#include <math.h>

#include <string.h>

#ihclude <graphics.h>

#include <process.h>

#include "st.h" int far arr_xl [15000] ,arr_yl [15000] ; void send_p ( unsigned , char * , unsigned ) _///sending fro corn-port int init_com ( int , unsigned ) ; void segment (int num_p,unsigned int far p_x[] ,unsigned int fa p_y[] ,char *p) extern float xmax,ymax; extern εhiftx; extern char *arg_εtr[]; int v_x[2] [1000] ; int v_y[2] [1000] ; int extrxy[2] [1000] ; int attr=0; // structure of binary file of data from graphic tablet struct point

{ unsigned x :13; unsigned y :13; int pen :6;

} Pnt;

// structure of binary file of coefficients of the cubic spline struct stroke

{ unsigned x :13 unsigned y :13, signed alfl:ll, signed alf2 :11, } εtrk; char buff [20] ; int j; int newp; int num_pointε; int ; float lastx,lasty; int lvx,lvy; FILE *fpl; int count,i; unsigned long εize_file = 0 int fl; int lpoint,fpoint; unsigned base; char *pl; char *exten={" .str"},- int ascii=0; /* test for extension ".εtr".If true - it iε the ASCII format * * for creating a stokes file */ strlwr(arg_εtr [2] ) ; if ( (strstr(arg_εtr[2] ,exten) ) !=NULL) aεcii=l; pl=p;

hic tablet * */ if ( fpl = fopen (arg εtr[l] , "wb") ) != ULL)

{ for (count=0;count<num_p ;count++)

{ int p; if (p x [count] <εhiftx)

{ ^" pnt .x= (unsigned) p_x [count] ;

P=l;

} elεe

{ pnt.x= (unsigned) (p_x [count] -εhiftx) ; p=0;

} pnt .y= (unsigned) p_y[ count] ; pnt.pen=p; f rite (&pnt,εizeof (pnt) ,l,fpl) ;

} fseek ( fpl , OL , SEEK_END ) ; εize_file = ftell ( fpl ) ; sprintf (buff, "%lu" , εize_file ) ; setcolor (13) ; settextstyle (0,0,1) ; outtextxy(168,40,buff) ; fcloεe(fpl) ;

} * */ lpoint=f point=0 ; fl = l; while (num_p>lpoint) for (i=fpoint ;p_x [i] >=shiftx && i<num_p;i++) ; attr= (unsigned) (p_x[i-l] -εhiftx) ; for (j=i;p_x [j] <shiftx && j<num_p;j++) arr_xl [j -i] = (f loat)p_x [j] ; arr_yl [ j -i] = (float )p_y [j] ; num_points=j -i ; newp=num_pointε ; fpoint=lpoint=j ; if (j<num p) {

/ * *

* process segmentation *

*/ lastx=arr_xl [num_pointε-l] ; lasty=arr_yl [num_points-l] ; extrxy[0] [0] =arr_xl [0] ; extrxyϊl] [0] =arr_yl [0] ;

oints-2] ; ointε-2] ;

num_pointε-- ; j=0; newp=num_jpointε ; for (i=0;i<newp-l; i++) {arr_yl [ j ] =arr_yl [i] ; arr_xl [j] =arr_xl [i] ; if (arr_yl [i] !=arr_yl [i+1] && arr_xl [i] ! =arr_xl [i + 1] ) j++; else num_jpoint ε - - ; j=0; newp=num_pointε ; for (i=0;i<newp-l;i++) { arr__yl [j] =arr_yl [i] ; arr_xl [j] =arr_xl [i] ; if (arr_yl [i] !=arr__yl [i+1] && arr_xl [i] !=arr_xl [i+1] ) j++; else num pointε-- ;

} / ***************************/

/♦A**************************/ newp=num_jooint ; m=0; εetcolor(13) ; ^' εetfillεtyle(l,9) ; fillellipse( (float) extrxy [0] [0] *639/xmax,479- (float) extrxy [1] 0] *400/ymax-28,l,l) ;

for (i=0;i<num_points-2;i++) if (arr_yl [i] <arr_yl[i+l] && arr_yl [i+1] >arr_yl [i+2] ) extrxy [0] [j] =arr_xl [i+1] ; extrxy [1] [j] =arr_yl [i+1] ; f illellipεe ( (float) extrxy [0] [j] *639/xmax, 479- (float) extrxy [1]

f illellipεe ( (float) extrxy [0] [j] *639/xmax,479- (float) extrxy [1] j] *400/ymax-28,l,l) ; v_x [0] [j] = (arr_xl [i+2] -arr_xl [i+1] ) ; v_y[0] [j] = (arr_yl[i+2] -arr_yl [i+1] ) ; v_x [1] [j] = (arr_xl [i+1] -arr_xl [i] ) ;

^V__Y HI ϊjϊ = (arr_yl [i+1] -arr_yl [i] ) ; j++;

} elεe if (arr xl[i]<arr xl[i+1] && arr xl [i+1] >arr xl [i+2] )

1 extrxy [0] [j] =arr_xl [i+1] ; extrxy [1] [j] =arr_yl [i+1] ; f illellipεe ( (float) extrxy [0] [j] *639/xmax,479- (float) extrxy [1] j]*400/ymax-28,l,l) ; v_x[0] [j] = (arr_xl[i+2] -arr_xl[i+l] ) ;

? elε^*e^• if (arr xl [i] >arr xl [i + 1] && arr xl [i + 1] <arr xl [i + 2] )

1 extrxy [0] [j] =arr_xl [i+1] ; ^" extrxy [1] [j] =arr_yl [i+1] ; fillellipse( (float) extrxy [0] [j] *639/xmax,479- (float) extrxy [1] [ j] *400/ymax-28,l,l) ; v_x[0] [j] = (arr_xl [i+2] -arr_xl [i+1] ) ; v_y{0] [j] = (arr_yl [i+2] -arr_yl [i+1] ) ; v_x[1] [j] = (arr_xl [i+1] -arr_xl [i] ) ; v_y [1] [j] = (arr_yl [i+1] -arr_yl [i] ) ;

}

} extrxy [0] [j]=lastx; extrxy [1] [j]=laεty; f illellipεe ( (float) extrxy [0] [ j] *639/xmax, 479- (float) extrxy [1] [ j] OO/ymax-28,1,1) ; v_x[l] [j]=lvx; v_y[l] [j]=lvy;

/* */

/*

* Calculating & writing a stroke file in binary or ASCII format*

*/

{ FILE *ff; if (fl)

{ . ff =ascii ? fopen (arg_εtr [2] , "w") :fopen (arg_εtr [2] , "wb") ; if (aεcii) f p r i n t f (ff," (((((( ((((((((((((((((((((((((((((((((((((((((((((((((((( (((((((^{( ( ( ( ( (}(^{( {}(⁽(⁽(\n") ir fl=0;

} else ff =ascii ? fopen (arg_εtr [2] J'a" ) :fopen (arg_str [2] , "ab") ; i f ( j = = 2 && extrxy [0] [0] = = extrxy [0] [l] && extrxy [1] [0] ==extrxy [1] [l] )

{ int temp ; j--; temp=j+ (attr<<9) ; me cpy(p, &temp,2) ; p+=2; if (aεcii) fprintf (ff, "%6d\n",temp) ; elεe fwrite (δtemp,εizeof (j) , l, ff) ; strk.x= (unsigned)extrxy[0] [0] ; εtrk.y= (unεigned) extrxy [1] [0] ; εtrk.alf 1=0; strk. alf 2=0; memcpy (p, &εtrk, εizeof (strk) ) ; p+=εizeof (εtrk) ; if (ascii) f p r i n t f ( f f , " % 4 d % 4 d % 6 d

%6d\n", extrxy [0] [0] , extrxy [1] [0] ,0,0) ; else fwrite (&strk, εizeof (strk) , 1, ff ) ;

} else {int temp; temp=j+ (attr<<9) ; memcpy(p,&temp,2) ; p+=2; if (ascii) fprintf (ff, "%6d\n",temp) ; elεe fwrite (&temp, εizeof (j) ,1, f f ) ; for (m=0;m<j-l;m++) { float vx , vy , vxl , vyl , al , al 1 , al2 , mods , modv , dlx , dly ; dlx=extrxy [0] [m] -extrxy [0] [m+1] ; dly=extrxy [1] [ ] -extrxy [1] [m+1] ; vx=v_x [0] [m] ; vy=v_y [0] [m] ; mods=sqrt (dlx*dlx+dly*dly) ; if (mods==0.0) mods=l; modv= (float) sqrt ( (vx*vx+vy*vy) ) ; if (modv==0.0) modv=l; all= (dlx*vx+dly*vy) ; if(all==0.0) all=0.01; al2= (-dly*vx+dlx*vy) ; all=al2/all;

/* */ vxl=v_x[l] [m+1] ; vyl=v_y [1] [m+1] ; modv= (float) sqrt (vxl*vxl+vyl*vyl) ; if (modv==0.0) modv=l; al= (dlx*vxl+dly*vyl) ; if(al==0.0) al=0.01; al2=(-dly*vxl+dlx*vyl) ; al=al2/al; al2=al; /*********/ if (m==j-2) al2=0.0; if (m==0) all=0.0; if (εign(al2)*al2<0.35 && sign(all) *all>l) all/=2.0; if (εign(all)*all<0.35 && εign (al2) *al2>l) al2/=2.0; if (εign(all)*all>5.0) all=εign (all) *l .0; if (εign(al2)*al2>5.0) al2=εign(al2) *1.0; strk. x= (unεigned) extrxy [0] [m] ; strk. y= (unεigned) extrxy [1] [m] ; εtrk.alfl= (εigned) (all*1023.0/5.0) ; εtrk.alf2= (εigned) (al2*1023.0/5.0) ; memcpy (p, &εtrk, εizeof (εtrk) ) ; p+ = ειzeof (εtrk) ; if (aεcii) fprintf (ff,"%4d %4d %6d %6d\n" , extrxy [0] [m] , extrxy [1] [m] , strk.alf 1, εtrk.alf2) ; else fwrite (&strk, εizeof (strk) ,l,ff) ; εtrk.x= (unεigned) extrxy [0] [m] ; strk. y= (unsigned) extrxy [1] [m] ; strk. alf 1=0, • εtrk. alf 2=0; memcpy (p, &strk, εizeof (strk) ) ; p+=εizeof (strk) ; if (ascii) fprintf (ff,"%4d %4d %6d %6d\n",extrxy[0] [m] ,extrxy[1] [m] , εtrk.alfl,εtrk.alf2) ; elεe fwrite (δstrk,εizeof (εtrk) ,1,ff) ;

} fclose (ff) ;

}

if( ! (base=init_com(2, 9600) ) ) exit(-1) ; εend_p( base , pi , (unεigned) (p-pl) ); if (ascii) {int leng; char ch[20] = {" " } ; fpl = fopen (arg_εtr [2] , "a" ) ; fεeek ( fpl , 0L , SEEK_END ) ; f p r i n t f

(fpl,")))))))))))))))))))))))))))))))))))))))))))))))))))))))) ))))))))))) )))))\n") ; fcloεe (fpl) ; εtrcpy (ch, arg_εtr [2] ) ; leng=εtrlen (ch) ; εtrcpy (&ch [leng- 3] , "bst") ; fpl = fopen (chJ'wb"); fwrite (pi, (unsigned) (p-pl) ,l,fpl) ; fcloεe (fpl) ;

} fpl = fopen (arg_εtr [2] ,"r") ; fεeek ( fpl , 0L , SEEK_END ) ; εize_file = ftell ( fpl ) ; εprintf (buff, "%lu", ize_file ) ; εetcolor(13) ; if (aεcii)

{ outtextxy(580, 0,buff) ; εprintf (buff, ^»%lu",p-pl ) ; outtextxy(368, 0,buff) ;

} elεe outtextxy(368,40,buff) ; fcloεe (fpl) ; free(p) ; free (pi) ; 3.1. Definition of coordinate svstems

\ <*o Y₀ ⁾

\

oyx-Intrinsic (Local) coordinate system describing the pen stroke.

OYX-Extrinsic (Global) coordinate system describing the world

(e.g computer screen)

RECONSTRUCTION GOAL : generation of a sequence of third-order (cubic) splines in local coordinate system and transition from the local oyx εysterr. to the global OYX system.

The directions of the OYX vectors

L₁, L₂, L_m that define the segmentation of a symbol are chosen in accordance with the OX axes direction (local system) .

In accordance with fig 2, the information needed for the reconstruction of a symbol consist of :

where are the angles between the corresponding vectors

and the vector n. The vectors

VI V.7: of the speed in the verric of the " skeleton " of the symbol (in the example in fig 2 , it iε the -.^zer "a").

Fax Pen

Reconstruction of hand imaging •

^include <εtdio.h>

^include <conio.h>

^include <math.h>

^include <εtring.h>

^include <proceεε.h>

#include <graphics.h>

#include "εt.h" float xmax=913.*5; float ymax=594.*5; int per=50; float v_x[2] [1000] ; float v_y[2] [1000] ; int dotted=0; unεigned char attr=0; int cols[5] ={8,9,10,12,14}; int extrxy[2] [1000] ;

//calculation of εpline function with 2 derivatives and 2 pointε float εpl(float ,float ,float ,float , float , float ,float ) ; void main (int num_arg, char *arg_εt [] )

// εtructure of binary file of cubic εpline coefficients struct stroke

{ unsigned x :13 unsigned y :13 εigned alfl :11 εigned alf2 :11 } εtrk;

•int gdriver = DETECT, gmode, errorcode; int j=0; int m; int i; int x; FILE *ff; float εeg,lseg; char *exten={" .εtr"} ; int aεcii=0;

/* teεt for file extenεion " .εtr" .If true - it iε the ASCII format * * for creating εtokeε file */ εtrlwr (arg_εt [1] ) ; if ( (εtrεtr (arg_εt [1] , exten) ) !=NULL) ascii=l; if (num_arg<=l) {pi intf ("Requires 1 parameter"); exit(l);} i f ( ( f f = a ε c i i ? fope (arg_st [1] , "r") :f open (arg_εt [1] , "rb") )==NULL) {printf ("File not found"); exit(l);} regiεterf arbgidriver (EGAVGA_driver_f ar) ; regiεterf arbgifont (εanεεerif_font_f ar) ; initgraph ( fcgdriver , &gmode , " " ) ; errorcode = graphreεult ( ) ; if (errorcode != grOk) { printf ("Graphicε error: %s\n" , grapherrormsg (errorcode) ) printf ("Press any key to halt : " ) ; getch() ; exit (1) ;

} εetbkcolor (15) ; εetcolor(1) ; rectangle (0,0, 639,479) ; line(0,35,639,35) ; setfillεtyle(l,8) ; bar(l, 1,638, 34) ; εettextεtyle(3, 0,4) ; εetcolor(11) ; outtextxy (142, -5, "The Stroke Interpreter") ; /A*********************************************/ if (aεcii)

{ c h a r *εtrl=" (((( ( ((((((((((((((((((((((((((((((((((((((((((((((((((

(((( (((((((( ( (((((((((("; int reε=l; while (res!=0) { char *εtr2; fεcanf (ff, "%78ε\n" , εtr2) ; res=strcmp (εtrl, εtr2) ;

while (1)

{int temp; if (aεcii) i=fεcanf (ff, "%d\n" , &temp) ; elεe i=fread(δctemp,εizeof (j) , 1, ff) ; j=temp & OxlFF; attr=temp>>9; if (i<=0)

{ fcloεe (ff) ; if (! getch ()) getch (); cloεegraph ( ) ; exit(l) ;

} i = 0;

{int vx,vy; while ( i<j )

{ if (ascii)

{ f ε c a n f ( f f , " % d % d % d

%d\n",&extrxy [0] [i] ,&extrxy[l] [i] ,&vx,&vy) ; v__x[0] [i] = ( (float) vx*5.0/1023.0) ; v__x[l] [i] = ( (float) vy*5.0/1023.0) ; elεe

{

(fread(&εtrk,εizeof (εtrk) ,1, ff) ) ; extrxy[0] [i]= (signed) strk.x; extrxy[1] [i] = (signed)εtrk.y;

//drawing splines

betε,modε, dlx,dly; [m] ; [m] ; modε=εgrt d x* lx+dly*dly ; xl=extrxy[0] [m] ; yl=extrxy[l] [m] ;

^• /* */

/★★★★★a**************************

*

* cos and sin of angle between local and global system

♦★★♦A*************************** / betc=dlx/modε; betε=-dly/mods; /*********/ for(i=0;i< ( (int)modε+1) ;i+=2) {float x0,y0; x=(float) i;

/A******************************* *

* calculation of local εpline function y=f (x) with parameterε:

* (start x & y,finiεh x & y,angelε in εtart & finiεh, argument x.

* y=εpl (0.0, 0.0, mods, 0.0, al2, all, ods -x) ;

*

* rotation of local system to global εyεtem χO=betc*x-bets*y+(float)extrxy[0] [m] ;

(float)extrxy[0] [m+1] *639/xmax, 79- (float)extrxy[1] [m₊1] *443/y max) ;

) ' i=i; /* } *

SPLINE RECONSTRUCTION :

Threshold conditions : (margin conditions) :

The εpline derivatives on the interval [0,11] edges, where ll module of the vector Ll (see fig 1.)

t≤rα₁ ^Bsino₁ cosα₁; c^α₂=sinα. cosα₂

where [ . ] vector product defined as

projections of the vectors

V v L on axes Ox, Oy, O∑ according to the significance of the splin on the interval [01,11] are : y(0) = y(ll) = 0

A spl ine is defined in the form of

where

are coef icients that are defined by margin conditions The equations system relatively unknown values

a_j, i=l ÷4

or

for

a₄=0 , a^ = tg ₂

The transformation to a global system is being done in accordance with the expressions:

^-εi βi cosβι .y,x⁾

The examples of the different letters reconstruction are given on fiα 3. #include <conio.h> #include <stdio.h> #include <math.h> #include <dos.h> #include <stdlib.h> #include <graphics.h> #include "qjib.h" #define DMAX 400

#define T 0.01

struct send_data {int period; float ampl; float incr_acc;

}; int reconstruct(send_data par[2],int t_count[2],float prev_dif[2])

{ static float px=-10000, py=-10000; float DIF[2]; int i; float acc[4]; float u1 ; float u2; float shift[2]; const float a1 1 =1 .071524; const float a12=0.1 1965; const float a21 =0.075; const float a22=-0.22502; const float b1 1 =13.333333; const float b12=13.333333; const float b21 =6.666667; const float b22=6.666667;

{ float dx; float dy; float x,y; setcolor(15);

/^* Restoration of oscillation signal ^*/ u1 =par[0].ampl^*sin(t_count[0]^*M_PI/par[0]. period); u2=par[1 ].ampl^*sin(t_count[1 ]^*M_PI/par[1 ]. period);

/^* Restoration of the movement of the center of oscillations ^*/

DIF[0]=t_count[0]/par[0].period+prev_dif[0]; DIF[1 ]=t_count[1 ]/par[1 ].period+prev_dif[1 j; /* reconstruction of the position of the pen's tip */ dx=(a11 *u1+a12*u2); dy=(a21^*u1 +a22^*u2); shift[0] = (b11 ^*DIF[0]+b12^*DIF[1]); shift[1] = (b21^*DIF[0]+b22*DIF[1]); if (px > -10000 && py > -10000)

{ // Addition of the movement of the center of oscillations to obtain XY coordinates. x=100+(dx+shift[0])*2; y=200+(dy+shift[1])*4; if (I) { line(px,py,x,y);

} // Saving coordinates of previouse point px=x; py=y; } else

{ px=100+(dx+shift[0])^*2; py=200+(dy+shift[1])^*4; }

} return 0;

}

APPENDIX I

The reconstruction procedure is performed in two stages:

- Restoring the signals.

- Reconstruction of the position of the pen's tip.

The restoration of the acceleration's signals from the data that was transmited by the pen (T,A,V for every segment) is done according the next formula: u_x(i,t) = u_x0 +u_x1 ; ιi_χo = u_x(i-1 ,T_xi.Λ + (V_x/T_xi) ^* t ; u_x1 = A_xi ^* SIN ((Pl/T_xi) ^* t) ;

u_y(i,t) = u_y0 +u_y1 ; u_y0 = u_y(i-1 ,T_yM) + (V_y/T_yi) ^* t ; u_y1 = A_yi * SIN ((Pl/T_yi) ^* t) ;

u_z(i,t) = u_z0 +u_z1 ; u_z0 = u_z(i-1 ,T_zi,) ₊ (V_z/T_zi) * t ; u_z1 = A_zi * SIN ((Pl/T_zi) * t) ; u_x,u_y,u_z are the restored signals of accelerometers. u_x0,u_y0,u_z0 are the restored movement of the center of oscilations. u_x1,u_y1,u_z1 are the restored signal of oscilations.

The reconstruction of the position of the pen's tip in XY plane is done by decomposition of two movements: the movement of the center of oscilations in XY plane (x₀,y₀) and the oscilation movement (contour of written symbol) (x^y . The calculation of these values is according the next formulas:

^X0 ^{= a}H ^Ux0 ^{+ a}i2 ^Uy0 y₀ = a₂₁ u_x0 + a₂₂ u_y0

*ι = bn^*u_x1 + b₁₂ ^*u_y1 Yι = b₂₁ ^*u_x1 + b₂₂ ^*u_y1

The parameters a^ , by vary from individual to individual and are received as the personal hand imaging characteristics of the writer at the beginning of a session.

Claims

C L A I M S

1. Communication apparatus for hand imaging comprising: apparatus for sensing features of hand imaging of an individual which features are highly characteristic of the indi¬ vidual but which also contain information relating to images represented thereby; and apparatus for providing a non-individual dependent output indicating the images in response to the sensed features.

2. Communication apparatus according to claim 1 and where¬ in said apparatus for sensing features is contained in a hand¬ held housing.

3. Communication apparatus according to claim 1 and where¬ in said apparatus for sensing features is contained in a tablet assembly.

4. Communication apparatus according to claim 1 and also comprising apparatus for communication of the non-individual dependent output.

5. Communication apparatus according to claim 2 and also comprising apparatus for communication of the non-individual dependent output.

6. Communication apparatus according to claim 3 and also comprising apparatus for communication of the non-individual

7Q dependent output.

7. Apparatus according to claim 4 and wherein said appara¬ tus for communication is operative to communicate information which can be used to reconstruct an individual's hand imaging style.

8. Apparatus according to claim 2 and wherein said sensing apparatus does not require a tablet.

9. Apparatus according to claim 4 and wherein said appara¬ tus for communication comprises a modem.

10. Apparatus according to claim 4 and wherein said appara¬ tus for communication is operative to communicate in a fax for¬ mat.

11. Apparatus according to claim 4 and wherein said appara¬ tus for communication is operative to communicate in a compressed non-raster format.

12. Apparatus according to claim 4 and wherein said appara¬ tus for communication is operative for wire communication.

13. Apparatus according to claim 4 and wherein said appara¬ tus for communication is operative for wireless communication.

14. Apparatus according to any of the preceding claims and wherein said apparatus for sensing features includes apparatus for sensing the instantaneous angle of motion during hand imag¬ ing.

15. Apparatus according to claim 14 and wherein said apparatus for providing a non-individual dependent output is operative for providing an output indication of strokes generated during hand imaging.

16. Communication apparatus for hand imaging comprising: apparatus for sensing features of hand imaging of an individual which features are highly characteristic of the indi¬ vidual but which also contain information relating to images represented thereby; and apparatus for providing an output indicating the images in response to the sensed features, and wherein said apparatus for sensing features includes apparatus for sensing the instantaneous angle of motion during hand imaging and providing an output indication of strokes generated thereby.

17. Apparatus for communicating hand imaging comprising hand-held apparatus for sensing motion and providing an output in a compressed form which can be transmitted by a conventional modem, LAN or other communications medium.

18. Apparatus according to any of claims 1-13 and 16-17 and also comprising apparatus for receiving communicated stroke content information and being operative for reconstructing there¬ from hand-imaging information.

19. Apparatus according to claim 14 and also comprising apparatus for receiving communicated stroke content information and being operative for reconstructing therefrom hand-imaging information.

20. Apparatus according to claim 15 and also comprising apparatus for receiving communicated stroke content information and being operative for reconstructing therefrom hand-imaging information.

21. Apparatus according to claim 18 and wherein said appa¬ ratus for receiving is operative to reconstruct hand-imaging information in three dimensions.

22. Apparatus according to claim 19 and wherein said appa¬ ratus for receiving is operative to reconstruct hand-imaging information in three dimensions.

23. Apparatus according to claim 20 and wherein said appa¬ ratus for receiving is operative to reconstruct hand-imaging information in three dimensions.

24. Communication apparatus for hand imaging including: apparatus for sensing motion during hand imaging and providing an output indication of stroke content in a compressed format; and apparatus for receiving communicated stroke content information and being operative to reconstruct therefrom hand- imaging information.

25. Apparatus according to claim 24 and wherein said appa¬ ratus for receiving is operative to reconstruct hand-imaging information in three dimensions.

26. A communication method for hand imaging comprising: sensing features of hand imaging of an individual which features are highly characteristic of the individual but which also contain information relating to images represented thereby; and providing a non-individual dependent output indicating the images in response to the sensed features.

27. A communication method for hand imaging comprising: sensing features of hand imaging of an individual which features are highly characteristic of the individual but which also contain information relating to images represented thereby; and providing an output indicating the images in response to the sensed features, and wherein said sensing features includes sensing the instantane¬ ous angle of motion during hand imaging and providing an output indication of strokes generated thereby. 28. A method for communicating hand imaging comprising hand-held apparatus for sensing motion and providing an output in a compressed form which can be transmitted by a conventional modem, LAN or other communications medium.

29. A communication method for hand imaging including: sensing motion during hand imaging and providing an output indication of stroke content in a compressed format; and receiving communicated stroke content information and reconstructing therefrom hand-imaging information.