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Poor handwriting is a diagnostic criterion for developmental coordination disorder. Typical of poor handwriting is its low overall quality and the high variability of the spatial characteristics of the letters, usually assessed with a subjective handwriting scale. Recently, Dynamic Time Warping (DTW), a technique originally developed for speech recognition, was introduced for pattern recognition in handwriting. The present study evaluates its application to analyze poor handwriting. Forty children attending Dutch mainstream primary schools were recruited and based on their scores on the Concise Evaluation Scale for Children's Handwriting (Dutch abbreviation: BHK), 20 good and 20 poor writers (of whom 13 were scheduled for handwriting intervention) were identified. The groups were matched for age (7-9 years), school grade (grades 2 and 3) and handedness. The children subsequently wrote sequences of the letter 'a' on a graphics tablet in three conditions (normal, fast, and accurate). Classical kinematics were obtained and for each individual letter DTW was used to calculate the distance from the mean shape. The DTW data revealed much higher variability in the letter forms of the poor writers that was independent of the kinematic results of larger trajectories, faster movements, and higher pen pressure. The current results suggest that DTW is a valid and objective technique for letter-form analysis in handwriting and may hence be useful to evaluate the rehabilitation treatments of children suffering from poor handwriting. In education research it may be exploited to explore how children (should) learn to write.

A well-established task in forensic writer identification focuses on the comparison of prototypical character shapes (allographs) present in handwriting. In order for a computer to perform this task convincingly, it should yield results that are plausible and understandable to the human expert. Trajectory matching is a well-known method to compare two allographs. This paper assesses a promising technique for so-called humancongruous trajectory matching, called Dynamic Time Warping (DTW). In the first part of the paper, an experiment is described that shows that DTW yields results that correspond to the expectations of human users. Since DTW requires the dynamics of the handwritten trace, the "online" dynamic allograph trajectories need to be extracted from the "offline" scanned documents. In the second part of the paper, an automatic procedure to perform this task is described. Images were generated from a large online dataset that provides the true trajectories. This allows for a quantitative assessment of the trajectory extraction techniques rather than a qualitative discussion of a small number of examples. Our results show that DTW can significantly improve the results from trajectory extraction when compared to traditional techniques.

In this paper we discuss mixed-method research in HCI. We report on an empirical literature study of the NordiCHI 2012 proceedings which aimed to uncover and describe common mixed-method approaches, and to identify good practices for mixed-methods research in HCI. We present our results as mixed-method research design patterns, which can be used to design, discuss and evaluate mixedmethod research. Three dominant patterns are identified and fully described and three additional pattern candidates are proposed. With our pattern descriptions we aim to lay a foundation for a more thoughtful application of, and a stronger discourse about, mixed-method approaches in HCI.

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This paper describes the use of Dynamic Time Warping (DTW) for classifying handwritten Tamil characters. Since DTW can match characters of arbitrary length, it is particularly suited for this domain. We built a prototype based classifier that uses DTW both for generating prototypes and for calculating a list of nearest prototypes. Prototypes were automatically generated and selected. Two tests were performed to measure the performance of our classifier in a writer dependent, and in a writer independent setting. Furthermore, several strategies were developed for rejecting uncertain cases. Two different rejection variables were implemented and using a Monte Carlo simulation, the performance of the system was tested in various configurations. The results are promising and show that the classifier can be of use in both writer dependent and writer independent automatic recognition of handwritten Tamil characters.

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