Inertial measurement components, which sense either acceleration or angular rate, are being embedded into common user interface devices more frequently as their cost continues to drop dramatically. These devices hold a number of advantages over other sensing technologies: they measure relevant parameters for human interfaces and can easily be embedded into wireless, mobile platforms. The work in this dissertation demonstrates that inertial measurement can be used to acquire rich data about human gestures, that we can derive efficient algorithms for using this data in gesture recognition, and that the concept of a parameterized atomic gesture recognition has merit. Further we show that a framework combining these three levels of description can be easily used by designers to create robust applications.

Smoothness is characteristic of coordinated human movements, and stroke patients ’ movements seem to grow more smooth with recovery. We used a robotic therapy device to analyze five different measures of movement smoothness in the hemiparetic arm of 31 patients recovering from stroke. Four of the five metrics showed general increases in smoothness for the entire patient population. However, according to the fifth Recent epidemiological data that have suggested increasing prevalence of stroke have prompted vigorous novel treatment trials and the use of unique brain-imaging tools to begin to understand the pathophysiology of stroke (Chollet et al., 1991; Dam et al., 1993). Most survivors of stroke will have impaired brain function and permanent levels of disability. As survival from stroke improves with modern medical care, the increasing number of these patients has also prompted the drive to understand the functional

This paper presents a hidden Markov model (HMM) based approach to on-line handwritten digit recognition using stroke sequences. In this approach, a character instance is represented by a sequence of symbolic strokes, and the representation is obtained by component segmentation and stroke classification. The component segmentation is based on the delta lognormal model of handwriting generation. The symbolic strokes are used for HMM multiple observation training or recognition. A training and recognition experiment has been conducted using the above techniques. 1. Introduction On-line handwriting recognition is an area of active research that aims at providing a dynamic means of communication with computers through a digitizing tablet and an electronic pen [9]. On-line handwritten scripts captured by the digitizing tablet consist of sequences of components that are pen-tip traces from pen-down to pen-up positions. As the shape of a script depends on pen-tip traces, many researchers use ...

Abstract. In this paper we present a method for synthesizing English handwritten textlines from ASCII transcriptions. The method is based on templates of characters and the Delta LogNormal model of handwriting generation. To generate a textline, first a static image of the textline is built by concatenating perturbed versions of the character templates. Then strokes and corresponding virtual targets are extracted and randomly perturbed, and finally the textline is drawn using overlapping strokes and delta-lognormal velocity profiles in accordance with the Delta LogNormal theory. The generated textlines are used as training data for a hidden Markov model based off-line handwritten textline recognizer. First results show that adding such generated textlines to the natural training set may be beneficial.

In style-constrained classification often there are only a few samples of each style and class, and the correspondences between styles in the training set and the test set are unknown. To avoid gross misestimates of the classifier parameters it is therefore important to model the pattern distributions accurately. We offer empirical evidence for intuitively appealing assumptions, in feature spaces appropriate for symbolic patterns, for (1) tetrahedral configurations of class means that suggests linear style-adaptive classification, (2) improved estimates of classification boundaries by taking into account the asymmetric configuration of the patterns with respect to the directions toward other classes, and (3) pattern-correlated style variability.

This paper presents an on-line hand-printed character recognition system, tested on datasets produced by the UNIPEN project, thus ensuring sufficient dataset size, author-independence and a capacity for objective benchmarking. New preprocessing and segmentation methods are proposed in order to derive a sequence of strokes for each character, following Z suggestions of biological models for handwriting. Variants of a novel neuro-fuzzy system, FasArt Fuzzy Adaptive System . ART-based , are used for both clustering and classification. The first task assesses the quality of segmentation and feature extraction techniques, together with an analysis of Shannon entropy. Experimental results for classification of the train_r01_02 UNIPEN dataset show real-time performance and a recognition rate of over 85%, exceeding slightly Fuzzy ARTMAP performance, and 5% inferior to the rate achieved by humans. q 1998 Elsevier Science B.V. All rights reserved. Keywords: On-line handwriting recognition; UNIPEN; Fuzzy neural networks; ART 1.

A new recognition system based on a neuro-fuzzy systcnn, called FasArt, is proposed in this paper. Satisfactory restfits were obtained using the train rO1 v02 UNIPEN dataset, together with a comparison with the recognition rates achieved by independent human testers. Two methods for segmenting handwritten components into strokes are proposed, with better experimental restfits for the method based on biological models of handwriting, in terms of consistency and network complexity. A systematic experimental study of different codification schemes is also described, based on Shannon entropy and clustering maps. Finally, some steps towards the construction of an allograph lexicon are shown, that exploit the generation of fuzzy-rules by FasArt architecture.

This thesis develops a mathematical basis for understanding the structure of on-line handwritten documents and explores some of the related problems in detail. With the increase in popularity of portable computing devices, such as PDAs and handheld computers, non-keyboard based methods for data entry are receiving more attention in the research communities and commercial sector. The most promising options are pen-based and speech-based inputs. Pen-based input devices generate handwritten documents which have on-line or dynamic (temporal) information encoded in them. Digitizing devices like Smart-Boards and computing platforms, which use pen-based input such as the IBM Thinkpad TransNote and Tablet PCs, create on-line documents. As these devices become available and affordable, large volumes of digital handwritten data will be generated and the problem of archiving and retrieving such data becomes an important concern. This thesis addresses the problem of on-line document understanding, which is an essential component of an effective retrieval algorithm. This thesis describes a mathematical model for representation of on-line handwritten

The kinematical information present in synthetically generated signatures is analyzed using the Sigma-Lognormal model and compared to the kinematical properties of real samples. Experiments are carried out on totally independent development and test sets and show a high degree of similarity between humanly produced and artificial signatures. One particular flaw is found in the velocity profile of synthetic signatures. Two possible solutions are proposed to improve the synthetic generation method using the Kinematic Theory of rapid human movements. 1.

The image of a word or a generic hand made drawing on a piece of paper is usually characterized by a series of interfering zones where the cursive trace intersects itself or printed lines already present on the writing surface. In this zone, the odometric information is ambiguous and any trivial inference on the original pen tip movement cannot be done. In this article, starting from some basic cognitive considerations, a general procedure is developed to analize a generic image of a word or a common hand made scribble. This approach allows to detect each ambiguity part of the image and then interpretate them to finally recover a part of the original temporal information. ii 1 Introduction The generation of a graphic shape on a writing surface during handwriting or drawing is the result of a complex motor planning process starting from an input allograph representation of the shape and then producing a sequence (partially overlapped) of primitive movements (called motor strokes) of t...