SymbolDesign: A User-centered Method to Design Pen-based Interfaces and Extend the Functionality of Pointer Input Devices

A method called "SymbolDesign" is proposed that can be used to design user-centered interfaces for pen-based input devices. It can also extend the functionality of pointer input devices such as the traditional computer mouse or the Camera Mouse, a camera-based computer interface. Users can create their own interfaces by choosing single-stroke movement patterns that are convenient to draw with the selected input device and by mapping them to a desired set of commands. A pattern could be the trace of a moving finger detected with the Camera Mouse or a symbol drawn with an optical pen. The core of the SymbolDesign system is a dynamically created classifier, in the current implementation an artificial neural network. The architecture of the neural network automatically adjusts according to the complexity of the classification task. In experiments, subjects used the SymbolDesign method to design and test the interfaces they created, for example, to browse the web. The experiments demonstrated good recognition accuracy and responsiveness of the user interfaces. The method provided an easily-designed and easily-used computer input mechanism for people without physical limitations, and, with some modifications, has the potential to become a computer access tool for people with severe paralysis.National Science Foundation (IIS-0093367, IIS-0308213, IIS-0329009, EIA-0202067

Design of an Offline Handwriting Recognition System Tested on the Bangla and Korean Scripts

This dissertation presents a flexible and robust offline handwriting recognition system which is tested on the Bangla and Korean scripts. Offline handwriting recognition is one of the most challenging and yet to be solved problems in machine learning. While a few popular scripts (like Latin) have received a lot of attention, many other widely used scripts (like Bangla) have seen very little progress. Features such as connectedness and vowels structured as diacritics make it a challenging script to recognize. A simple and robust design for offline recognition is presented which not only works reliably, but also can be used for almost any alphabetic writing system. The framework has been rigorously tested for Bangla and demonstrated how it can be transformed to apply to other scripts through experiments on the Korean script whose two-dimensional arrangement of characters makes it a challenge to recognize. The base of this design is a character spotting network which detects the location of different script elements (such as characters, diacritics) from an unsegmented word image. A transcript is formed from the detected classes based on their corresponding location information. This is the first reported lexicon-free offline recognition system for Bangla and achieves a Character Recognition Accuracy (CRA) of 94.8%. This is also one of the most flexible architectures ever presented. Recognition of Korean was achieved with a 91.2% CRA. Also, a powerful technique of autonomous tagging was developed which can drastically reduce the effort of preparing a dataset for any script. The combination of the character spotting method and the autonomous tagging brings the entire offline recognition problem very close to a singular solution. Additionally, a database named the Boise State Bangla Handwriting Dataset was developed. This is one of the richest offline datasets currently available for Bangla and this has been made publicly accessible to accelerate the research progress. Many other tools were developed and experiments were conducted to more rigorously validate this framework by evaluating the method against external datasets (CMATERdb 1.1.1, Indic Word Dataset and REID2019: Early Indian Printed Documents). Offline handwriting recognition is an extremely promising technology and the outcome of this research moves the field significantly ahead

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders

Occupational Therapy Handwriting Practice in South Korea

Background. Internationally, handwriting difficulty is a common issue among children. Occupational therapists are involved in helping children to improve their handwriting. Previous studies have summarised occupational therapy research and practice in handwriting, but these have not included information about occupational therapy practice for children’s handwriting in South Korea. To understand the nature of practice and identify the scope of evidence relating to South Korean occupational therapy for children with handwriting difficulties, a review of published literature on this topic is required. Methods. A scoping review was conducted to identify and summarize published literature on occupational therapy paediatric handwriting practice in South Korea. A detailed context of the review was provided in a background chapter (Chapter 1 “Introduction”). The introduction provided comprehensive information about the hand, handwriting, South Korea and the occupational therapy profession in South Korea to define terms and to help provide an understanding of occupational therapy practice conducted in Korea. Chapter 2 “A scoping review of occupational therapy handwriting literature” is presented in the form of a manuscript for submission to a peer-reviewed journal (Occupational Therapy International). This background, the gap in evidence and research design used is presented. This study used a scoping review methodological framework suggested by Arksey and O’Malley (2005). This five-step framework was followed. First, the research question was identified; second, a search strategy was designed in Korean and English, and implemented in three databases which published or may have published Korean occupational therapy research. Third, after inspection a total of 22 articles were selected for inclusion from 151 sources. Fourth, a data-extraction form in Excel™ was created and this recorded the characteristics of each of these studies. At the last stage, a descriptive analysis of numerical data and thematic analysis were used to collate, summarise and synthesise the data from the 22 included papers. Results. Key findings of the scoping review demonstrate that hospitals and school-based settings were the most commonly studied service sites. Most studies were with Korean children with cerebral palsy. Standardized assessments were predominantly used, and these measured various performance components, rather than the “task” or “activity” of handwriting. Author-designed handwriting assessments which were reported to be based on previous studies were frequently used for measurement of handwriting quality. These did not ⅳ go through standardisation or validation processes. A sensory integration approach was the most popular approach to intervention, and the most targeted performance component of handwriting was fine-motor skills. Most study designs were of low research rigour in the evidence-based hierarchy. This study highlights that there is a diverse approach to assessments and intervention in Korean occupational therapy handwriting research, indicating that there is no consensus on the best handwriting approach in Korean occupational therapy literature. Conclusion. Most of the found evidence was focussed on clinical samples and used a sensory integrative approach. This is different to international occupational therapy research literature (which used standardised instruments) which focused mostly on typically developing children and used a wide number of conceptual approaches. Korean research was similar in the low level of research evidence generated. In the future, Korean occupational therapy handwriting research should use rigorous designs and should use assessments to accommodate the cultural and linguistic uniqueness of Korea. This will provide more opportunities to enhance the diversity of evidence on handwriting research

Substroke Matching by Segmenting and Merging for Online Korean Cursive Character Recognition

The Korean character is composed of several alphabets in two-dimensional formation and the total number of Korean characters exceeds eleven thousand. Therefore, the previous approaches to Korean cursive characters pay most of their attention to segmenting a character into alphabets accurately. However, it is difficult because the boundaries of alphabets are not apparent in most cases. We propose an alphabet-based method without assuming accurate alphabet segmentation. In the proposed method, a cursive character is segmented into substrokes by a set of segmenting conditions. Then it is matched with the reference substrokes generated from alphabet models and ligatures by segmenting and merging in the process of recognition. Among substrokes, a certain substroke can be either an alphabet itself a part of alphabet or a composite of the alphabet and ligature. We applied the proposed method to 5000 Korean characters and got the result of 83.4% for the first rank and 89.2% for the top 5 result candidates with the speed of 0.17 seconds on average per character on a PC which uses Intel Pentium 90 Mhz CPU

기기 상에서의 심층 신경망 개인화 방법

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. Egger, Bernhard.There exist several deep neural network (DNN) architectures suitable for embedded inference, however little work has focused on training neural networks on-device. User customization of DNNs is desirable due to the difficulty of collecting a training set representative of real world scenarios. Additionally, inter-user variation means that a general model has a limitation on its achievable accuracy. In this thesis, a DNN architecture that allows for low power on-device user customization is proposed. This approach is applied to handwritten character recognition of both the Latin and the Korean alphabets. Experiments show a 3.5-fold reduction of the prediction error after user customization for both alphabets compared to a DNN trained with general data. This architecture is additionally evaluated using a number of embedded processors demonstrating its practical application.내장형 기기에서 심층 신경망을 추론할 수 있는 아키텍처들은 존재하지만 내장형 기기에서 신경망을 학습하는 연구는 별로 이뤄지지 않았다. 실제 환경을 반영하는 학습용 데이터 집합을 모으는 것이 어렵고 사용자간의 다양성으로 인해 일반적으로 학습된 모델이 충분한 정확도를 가지기엔 한계가 존재하기 때문에 사용자 맞춤형 심층 신경망이 필요하다. 이 논문에서는 기기상에서 저전력으로 사용자 맞춤화가 가능한 심층 신경망 아키텍처를 제안한다. 이러한 접근 방법은 라틴어와 한글의 필기체 글자 인식에 적용된다. 라틴어와 한글에 사용자 맞춤화를 적용하여 일반적인 데이터로 학습한 심층 신경망보다 3.5배나 작은 예측 오류의 결과를 얻었다. 또한 이 아키텍처의 실용성을 보여주기 위하여 다양한 내장형 프로세서에서 실험을 진행하였다.Abstract i Contents iii List of Figures vii List of Tables ix Chapter 1 Introduction 1 Chapter 2 Motivation 4 Chapter 3 Background 6 3.1 Deep Neural Networks 6 3.1.1 Inference 6 3.1.2 Training 7 3.2 Convolutional Neural Networks 8 3.3 On-Device Acceleration 9 3.3.1 Hardware Accelerators 9 3.3.2 Software Optimization 10 Chapter 4 Methodology 12 4.1 Initialization 13 4.2 On-Device Training 14 Chapter 5 Implementation 16 5.1 Pre-processing 16 5.2 Latin Handwritten Character Recognition 17 5.2.1 Dataset and BIE Selection 17 5.2.2 AE Design 17 5.3 Korean Handwritten Character Recognition 21 5.3.1 Dataset and BIE Selection 21 5.3.2 AE Design 21 Chapter 6 On-Device Acceleration 26 6.1 Architecure Optimizations 27 6.2 Compiler Optimizations 29 Chapter 7 Experimental Setup 30 Chapter 8 Evaluation 33 8.1 Latin Handwritten Character Recognition 33 8.2 Korean Handwritten Character Recognition 38 8.3 On-Device Acceleration 40 Chapter 9 Related Work 44 Chapter 10 Conclusion 47 Bibliography 47 요약 55 Acknowledgements 56Maste