Wireless Text Entry and Mouse System for the Handicapped

Of late, many text entry systems in users' languages with various text entry rate enhancement strategies are being proposed. To evaluate the effectiveness of such text entry systems, measuring error correction efficiency in addition to text entry rate have been advocated by researcher. Existing metrics for evaluating text entry errors are found inaccurate to evaluate text entry systems augmented with word prediction. This work attempts to bridge this gap. In this work, we redefine existing error classes as well as error quantifying metrics. In addition to this, we also introduce five different errors classes and six new metrics relevant to text entry error evaluation in the context of text entry systems augmented with word prediction. We substantiate the validity of error classes and efficacy of the metrics with a sufficient number of instances.

Designing intelligent interactive text entry systems often relies on factors that are difficult to estimate or assess using traditional HCI design and evaluation methods. We introduce a complementary approach by adapting function structure models from engineering design. We extend their use by extracting controllable and uncontrollable parameters from function structure models and visualizing their impact using envelope analysis. Function structure models allow designers to understand a system in terms of its functions and flows between functions and decouple functions from function carriers. Envelope analysis allows the designer to further study how parameters affect variables of interest, for example, accuracy, keystroke savings and other dependent variables. We provide examples of function structure models and illustrate a complete envelope analysis by investigating a parameterized function structure model of predictive text entry. We discuss the implications of this design approach for both text entry system design and for critique of system contributions.

Intelligent text entry systems, including the now-ubiquitous predictive keyboard, can make text entry more efficient, but little is known about how these systems affect the content that people write. To study how predictive text systems affect content, we compared image captions written with different kinds of predictive text suggestions. Our key findings were that captions written with suggestions were shorter and that they included fewer words that that the system did not predict. Suggestions also boosted text entry speed, but with diminishing benefit for faster typists. Our findings imply that text entry systems should be evaluated not just by speed and accuracy but also by their effect on the content written.

Non-speaking individuals with motor disabilities heavily rely on augmentative and alternative communication (AAC) text entry systems to communicate. However, designing, developing, and evaluating AAC text entry systems for users and researchers gives rise to several challenges, such as the difficulty of eliciting requirements for a highly heterogeneous user population using a wide variety of assistive devices. This research aims to address such challenges and bridge the gaps between AAC stakeholders while exploring the potential of artificial intelligence (AI)-powered AAC systems to cater to diverse AAC needs and wants. To achieve these goals, we propose an imperfect surrogate user model for AAC system design and evaluation, develop a dedicated machine learning language model for AAC purposes, and design an AI-driven text entry system Tinkerable AAC (TAAC) which is ready for user testing. The objective of these efforts is to ultimately reduce the communication gap between AAC users and their speaking partners.

Split keyboards are widely used on hand-held touchscreen devices (e.g., tablets). However, typing on a split keyboard often requires eye movement and attention switching between two halves of the keyboard, which slows users down and increases fatigue. We explore peripheral typing, a superior typing mode in which a user focuses her visual attention on the output text and keeps the split keyboard in peripheral vision. Our investigation showed that peripheral typing reduced attention switching, enhanced user experience and increased overall performance (27 WPM, 28% faster) over the typical eyes-on typing mode. This typing mode can be well supported by accounting the typing behavior in statistical decoding. Based on our study results, we have designed GlanceType, a text entry system that supported both peripheral and eyes-on typing modes for real typing scenario. Our evaluation showed that peripheral typing not only well co-existed with the existing eyes-on typing, but also substantially improved the text entry performance. Overall, peripheral typing is a promising typing mode and supporting it would significantly improve the text entry performance on a split keyboard.

BMK stick, a Bluetooth mouse with a keyboard is a joystick-type input device based on an inertial measurement unit (IMU) sensor that recognizes human hand movements along with the combinations of five buttons and enables users to input text with one hand. It is designed as an input device for computers and virtual environments. The BMK stick has two modes, mouse mode (mode 0) and keyboard mode (mode 1), which can be set by pushing a button. In mode 0, the IMU sensor extracts the value of the user’s hand motion with the 3D Euler angle. The extracted values are then converted into two-dimensional Cartesian coordinates through a built-in algorithm in the microcontroller unit (MCU). The converted value is mapped onto the computer screen to control the mouse pointer. In mode 1, text can be entered with with one hand using a five-button combination by using an AEIOU key-map provided by a commercial text-entry device, TAP. Two experiments were conducted to evaluate the performance of the BMK stick. In the first experiment, the mouse pointing performance was compared with three existing commercial ones. In the second experiment, the text-entry rate was evaluated in words per minute (WPM) compared with the two existing commercial one-handed keyboard input devices. The result shows that our proposed device in the first experiment performed better than the other commercial products. In the second experiment, even though some experimenters had some difficulties in manipulating our device, it produced more WPM than the commercial ones.

Functional written communication, an important goal in the rehabilitation of persons with tetraplegia, frequently is met through the use of personal computers and alternative computer access systems. To make informed decisions about alternative access systems, the therapist needs information on the efficacy of the available choices. The purpose of this study was to investigate the effectiveness of two commercially available systems for text entry, the traditional mouthstick and the Prentke Romich HeadMaster. Participants were a 25-year-old man and 76-year-old woman who both functioned at a C5 neurological level. Neither participant had previous experience with either system for text entry. A single-subject research design was used whereby Participant 1 experienced six phases of treatment (i.e., CBCBCB, where C = mouthstick and B = HeadMaster), and Participant 2 experienced four phases of treatment (i.e., BCBC). Participant 1 achieved a maximum rate of text entry of 5.85 wpm with both the HeadMaster and the mouthstick, whereas Participant 2 achieved a maximum rate of 7.15 wpm with the mouthstick and 4.85 wpm with the HeadMaster. Results from this study were similar to the results from previous comparison studies of persons with severe disabilities who had no experience with alternative computer access systems. Both participants were able to use both systems successfully; however, their respective rates of text entry were too slow to be functional in most employment situations.

Traditional physics labs in high school suffer from slow data acquisition so that dynamic values of variables are hidden from students. Modern mobile devices such as Lego Mindstorms NXT, smartphones and Arduino can acquire data at a fast rate and can be used to measure dynamic variables in physics experiments. A case in point is the changing angle of a pendulum experiment. With a tool called InduLab, students in three groups using the mobile devices mentioned above in pendulum experiments collected data and built their model s with the data. Results reviewed the Arduino group achieved the highest success rate of building correct models, followed by the smartphone group and then the NXT group. This suggested that modern low-cost mobile devices can be used to improve physics labs in high school.

Text entry has long been an indispensable part of people’s lives; notwithstanding, in virtual reality (VR), an efficient and handy text entry method for such an environment is still wanted. There are two negative factors in the very majority of existing text entry methods: 1) constrained by two-dimensional mapping; 2) must manipulate with both hands to ensure efficiency. Few are employing the three-dimensional space a virtual environment provides, with which text entry could perform much better; also, even with several methods enabling one-hand manipulation, the trade-off is off-balance when sacrificing performance. Therefore, we propose an innovative text entry method to achieve a faster speed, higher accuracy, and better user experience. We design a cross-like layout to reduce the average distance of spatial displacement when selecting characters. In selecting and entering characters, a user simply rotates the wrist and points the embodied controller to one of the seven character blocks in the virtual environment; afterward, within the selected block, the user enters the target character or auto-completion candidate word via two basic trackpad interactions, touching and clicking. We evaluate our CrossKeys mainly based on two criteria: efficiency, task load, and three tasks: learnability and performance test, fatigue test, and evaluation of in-motion performance. To evaluate efficiency, we analyze words per minute (WPM) and error rate (NCER, TER); to evaluate task load, we analyze NASA-TLX and Simulator Sickness Questionnaire (SSQ) results. According to the data from our participants after only 2 hours of first-time training, our CrossKeys performs well with an average WPM of 17.73, a peak WPM of 24.73, and an error rate (NCER) of 0.30% along with a low task load.

Most text entry methods require users to have physical devices within reach. In many contexts of use, such as around large displays where users need to move freely, device-dependent methods are ill suited. We explore how selection-based text entry methods may be adapted for use in mid-air. Initially, we analyze the design space for text entry in mid-air, focusing on single-character input with one hand. We propose three text entry methods: H4 Mid-Air (an adaptation of a game controller-based method by MacKenzie et al. [21]), MultiTap (a mid-air variant of a mobile phone text entry method), and Projected QWERTY (a mid-air variant of the QWERTY keyboard). After six sessions, participants reached an average of 13.2 words per minute (WPM) with the most successful method, Projected QWERTY. Users rated this method highest on satisfaction and it resulted in the least physical movement.KeywordsText entrymid-air interaction techniqueslarge high-resolution displaysHuffman codingmultitap

In this paper we investigate text entry performance for mobile touchscreen devices with emulated QWERTY keyboards, with special emphasis on interaction style and screen size. When addressing interaction style, we are referring to the five most common combinations of hands postures and device orientations while executing text entry tasks. Both single-finger and two-thumb methods for typing in portrait and/or landscape layout are considered. As for screen sizes, several classes of popular mobile devices are examined, specifically smartphones and tablets with smaller and larger form factor. In addition, the mobile device emulator is included in the study, in order to report the comparative analysis of text entry with an actual device and its emulation-based counterpart. The touchscreen desktop monitor was used so as to provide touch input for the device emulator. Results obtained from experimental testing, supported by thorough data analysis, provide a valuable insight into the user behavior when typing on touchscreens.

We present Parakeet, a system for continuous speech recognition on mobile touch-screen devices. The design of Parakeet was guided by computational experiments and validated by a user study. Participants had an average text entry rate of 18 words-per-minute (WPM) while seated indoors and 13 WPM while walking outdoors. In an expert pilot study, we found that speech recognition has the potential to be a highly competitive mobile text entry method, particularly in an actual mobile setting where users are walking around while entering text.

Over the last three decades, eye gaze has become an important modality of text entry in large and small display digital devices covering people with disabilities beside the able-bodied. Despite of many tools being developed, issues like minimizing dwell time, visual search time and interface area, eye-controlled mouse movement stability etc. are still points of concern in making any gaze typing interface more user friendly, accurate and robust. In this paper, we propose EyeK, a gaze-based text entry system which diminishes dwell time and favors to mitigate visual search time. Performance evaluation shows that proposed interface achieves on an average 15% higher text entry rate over the existing interfaces. As designed, the proposed interface can effortlessly be suited in medium-sized display devices like Tablet PC, PDA etc. Also, the developed system can be used by the people with motor disabilities.

In this paper, the two design techniques of the embedded system which provides a wireless image observation with temporary ad-hoc network are proposed and developed. The first method is based on the embedded system design technique for a nearly real-time wireless short observation application, having a specific remote monitoring node with a built-in image processing function, and having the maximum rate of 1 fps (frame per second) wireless image transmission capability of a 160x128size image. The second technique uses the embedded system for a general wireless long observation application, consisting of the main node, the remote monitoring node, and the system controller with built-in image processing function, and the capability of the wireless image transmission rate of 1/3 fps. The proposed system uses the wireless ad-hoc network which is widely accepted as a short range, low power, and bidirectional digital communication, the hardware are consisted of the general developed modules, a small digital camera, and a PC, and the embedded software based upon the Zigbee stack and the user interface software are developed and tested on the implemented module. The wireless environment analysis and the performance results are presented. 논문 14-39A-05-08 The Journal of Korea Information and Communications Society '14-05 Vol.39A No.05 http://dx.doi.org/10.7840/kics.2014.39A.5.271 271 ※ 본 연구는 2010년도 정부(교육과학기술부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(No.2010-0023678) ° First Author and Corresponding Author : Hallym University 전자공학과 통신시스템 연구실, yulee@hallym.ac.kr, 정회원 * 한림대학교 전자공학과대학원, sssong@hallym.ac.kr, jwpark9023@hallym.ac.kr 논문번호:KICS2014-02-068, Received February 28, 2014; Reviewed April 23, 2014; Accepted April 23, 2014 The Journal of Korea Information and Communications Society '14-05 Vol.39A No.05

In previous work on mobile learning, students used cellphones and pocket computers (PDAs) primarily to view study materials and answer quizzes. But anecdotes imply that Japanese students type faster on cellphones than on desktop PCs, suggesting that students could use mobile devices to take notes and write reports. This paper is a first quantitative investigation into the ability of Japanese students to enter text on mobile devices. In 2-minute transcription tasks, 24 Japanese university students typed Japanese: English at 8:7 wpm on miniature QWERTY thumb keyboards, 10:9 on onscreen QWERTY keyboards, 17:5 on cellphones, 23:14 on desktop PCs, and 31:30 with pencil and paper. 5-minute composition speeds were slightly less in the student's native Japanese, and about half in English. Transcription errors were rare in Japanese, but more frequent in English, especially on mobile devices. Students preferred typing on desktops and cellphones. This data suggests that Japanese students could take notes and write reports on their mobile phones, but would require training before using PDAs and writing in English. Future work includes longitudinal studies learning various input methods, including handwriting recognition.