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"Penmanship"
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High-performance brain-to-text communication via handwriting
Brain–computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak. So far, a major focus of BCI research has been on restoring gross motor skills, such as reaching and grasping
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or point-and-click typing with a computer cursor
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. However, rapid sequences of highly dexterous behaviours, such as handwriting or touch typing, might enable faster rates of communication. Here we developed an intracortical BCI that decodes attempted handwriting movements from neural activity in the motor cortex and translates it to text in real time, using a recurrent neural network decoding approach. With this BCI, our study participant, whose hand was paralysed from spinal cord injury, achieved typing speeds of 90 characters per minute with 94.1% raw accuracy online, and greater than 99% accuracy offline with a general-purpose autocorrect. To our knowledge, these typing speeds exceed those reported for any other BCI, and are comparable to typical smartphone typing speeds of individuals in the age group of our participant (115 characters per minute)
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. Finally, theoretical considerations explain why temporally complex movements, such as handwriting, may be fundamentally easier to decode than point-to-point movements. Our results open a new approach for BCIs and demonstrate the feasibility of accurately decoding rapid, dexterous movements years after paralysis.
A brain–computer interface enables rapid communication through neural decoding of attempted handwriting movements in a person with paralysis.
Journal Article
The missing ink : the lost art of handwriting
When Philip Hensher realized that he didn't know what one of his closest friend's handwriting looked like, he felt that something essential was missing from their friendship. It dawned on him that, having abandoned fountain pens for keyboards, we have lost one of the ways by which we come to recognize and know another person. The Missing Ink tells the story of this endangered art. Hensher reflects on what handwriting can tell us about personality and personal history: are your own letters neat and controlled or messy and inconsistent? Did you shape your penmanship in worshipful imitation of a popular girl at school, or do you still use the cursive you were initiated into in the second grade? Hensher guides us through Arabic calligraphy and the story of the nineteenth-century handwriting evangelists who traveled across America to convert the masses to the moral worth of copperplate; he pays tribute to the warmth and personality of a handwritten note. With the teaching of handwriting now required in only five states, and many expert typists barely able to hold a pen, the future of handwriting is in jeopardy. Or is it?
Book
The Semiotics of Emoji
Shortlisted for the BAAL Book Prize 2017 Emoji have gone from being virtually unknown to being a central topic in internet communication. What is behind the rise and rise of these winky faces, clinking glasses and smiling poos? Given the sheer variety of verbal communication on the internet and English's still-controversial role as lingua mundi for the web, these icons have emerged as a compensatory universal language. The Semiotics of Emoji looks at what is officially the world's fastest-growing form of communication. Emoji, the colourful symbols and glyphs that represent everything from frowning disapproval to red-faced shame, are fast becoming embedded into digital communication. Controlled by a centralized body and regulated across the web, emoji seems to be a language: but is it? The rapid adoption of emoji in such a short span of time makes it a rich study in exploring the functions of language. Professor Marcel Danesi, an internationally-known expert in semiotics, branding and communication, answers the pertinent questions. Are emoji making us dumber? Can they ultimately replace language? Will people grow up emoji literate as well as digitally native? Can there be such a thing as a Universal Visual Language? Read this book for the answers.
eBook
The John Hancock Club
Third-grader Sean McFerrin wants to be part of the good penmanship club, but it all depends on how well he learns the new cursive writing.
Book
A novel feature extraction method based on dynamic handwriting for Parkinson’s disease detection
Parkinson’s disease (PD) is a common disease of the elderly. Given the easy accessibility of handwriting samples, many researchers have proposed handwriting-based detection methods for Parkinson’s disease. Extracting more discriminative features from handwriting is an important step. Although many features have been proposed in previous researches, the insight analysis of the combination of handwriting’s kinematic, pressure, and angle dynamic features is lacking. Moreover, most existing feature is incompletely represented, with feature information lost. Therefore, to solve the above problems, a new feature extraction approach for PD detection is proposed using handwriting. First, built on the kinematic, pressure, and angle dynamic features, we propose a moment feature by composed these three types of features, an overall representation of these three types of features information. Then, we proposed a feature extraction method to extract time-frequency-based statistical (TF-ST) features from dynamic handwriting features in terms of their temporal and frequency characteristics. Finally, we proposed an escape Coati Optimization Algorithm (eCOA) for global optimization to enhance classification performance. Self-constructed and public datasets are used to verify the proposed method’s effectiveness respectively. The experimental results showed an accuracy of 97.95% and 98.67%, a sensitivity of 98.15% (average) and 97.78%, a specificity of 99.17% (average) and 100%, and an AUC of 98.66% (average) and 98.89%. The code is available at https://github.com/dreamhcy/MLforPD .
Journal Article
Stacey Coolidge's fancy-smancy handwriting : a story about staying true to yourself
Carolyn loves second grade until her difficulty with handwriting shakes her confidence. Stacey Coolidge is the best at handwriting. She hardly ever uses her eraser. But Carolyn isn't doing as well. Carolyn has been practicing cursive handwriting every day for weeks, and not only is she not going to get to play with Frederick, the class guinea pig, but her handwriting is also not much better than a guinea pig's. It's a good thing that her teacher, Mrs. Thompson, is able to turn her frustration into confidence.
BOOK
The internal structure of handwriting proficiency in beginning writers
Fluent and automatized handwriting frees cognitive resources for more complex elements of writing (i.e., spelling or text generation) or even math tasks (i.e., operating) and is therefore a central objective in primary school years. Most previous research has focused on the development of handwriting automaticity across the school years and characteristics of handwriting difficulties in advanced writers. However, the relative and absolute predictive power of the different kinematic aspects for typically developing beginning handwriting remains unclear. The purpose of the present study was to investigate whether and to what extent different kinematic aspects contribute to handwriting proficiency in typically developing beginning handwriters. Further, we investigated whether gender, socioeconomic background, or interindividual differences in executive functions and visuomotor integration contribute to children’s acquisition of handwriting. Therefore, 853 first-grade children aged seven copied words on a digitized tablet and completed cognitive performance tasks. We used a confirmatory factor analysis to investigate how predefined kinematic aspects of handwriting, specifically the number of inversions in velocity (NIV), pen stops, pen lifts, and pressure on the paper, are linked to an underlying handwriting factor. NIV, pen stops, and pen lifts showed the highest factor loadings and therefore appear to best explain handwriting proficiency in beginning writers. Handwriting proficiency was superior in girls than boys but, surprisingly, did not differ between children from low versus high socioeconomic backgrounds. Handwriting proficiency was related to working memory but unrelated to inhibition, shifting, and visuomotor integration. Overall, these findings highlight the importance of considering different kinematic aspects in children who have not yet automatized pen movements. Results are also important from an applied perspective, as the early detection of handwriting difficulties has not yet received much research attention, although it is the base for tailoring early interventions for children at risk for handwriting difficulties.
Journal Article