Speech Recognition Technology: Improving Speed and Accuracy of Emergency Medical Services Documentation to Protect Patients

Because hospital errors, such as mistakes in documentation, cause one in six deaths each year in the United States, the accuracy of health records in the emergency medical services (EMS) must be improved. One possible solution is to incorporate speech recognition (SR) software into current tools used by EMS first responders. The purpose of this research was to determine if SR software could increase the efficiency and accuracy of EMS documentation to improve the safety of patients of EMS. An initial review of the literature on the performance of current SR software demonstrated that this software was not 99% accurate, and therefore, errors in the medical documentation produced by the software could harm patients. The literature review also identified weaknesses of SR software that could be overcome so that the software would be accurate enough for use in EMS settings. These weaknesses included the inability to differentiate between similar phrases and the inability to filter out background noise. To find a solution, an analysis of natural language processing algorithms showed that the bag-of-words post processing algorithm has the ability to differentiate between similar phrases. This algorithm is best suited for SR applications because it is simple yet effective compared to machine learning algorithms that required a large amount of training data. The findings suggested that if these weaknesses of current SR software are solved, then the software would potentially increase the efficiency and accuracy of EMS documentation. Further studies should integrate the bag-of-words post processing method into SR software and field test its accuracy in EMS settings

A Novel Gesture-based CAPTCHA Design for Smart Devices

CAPTCHAs have been widely used in Web applications to prevent service abuse. With the evolution of computing environment from desktop computing to ubiquitous computing, more and more users are accessing Web applications on smart devices where touch based interactions are dominant. However, the majority of CAPTCHAs are designed for use on computers and laptops which do not reflect the shift of interaction style very well. In this paper, we propose a novel CAPTCHA design to utilise the convenience of touch interface while retaining the needed security. This is achieved through using a hybrid challenge to take advantages of human’s cognitive abilities. A prototype is also developed and found to be more user friendly than conventional CAPTCHAs in the preliminary user acceptance test

Using Technology Enabled Qualitative Research to Develop Products for the Social Good, An Overview

This paper discusses the potential benefits of the convergence of three recent trends for the design of socially beneficial products and services: the increasing application of qualitative research techniques in a wide range of disciplines, the rapid mainstreaming of social media and mobile technologies, and the emergence of software as a service. Presented is a scenario facilitating the complex data collection, analysis, storage, and reporting required for the qualitative research recommended for the task of designing relevant solutions to address needs of the underserved. A pilot study is used as a basis for describing the infrastructure and services required to realize this scenario. Implications for innovation of enhanced forms of qualitative research are presented

Crossmodal audio and tactile interaction with mobile touchscreens

Touchscreen mobile devices often use cut-down versions of desktop user interfaces placing high demands on the visual sense that may prove awkward in mobile settings. The research in this thesis addresses the problems encountered by situationally impaired mobile users by using crossmodal interaction to exploit the abundant similarities between the audio and tactile modalities. By making information available to both senses, users can receive the information in the most suitable way, without having to abandon their primary task to look at the device. This thesis begins with a literature review of related work followed by a definition of crossmodal icons. Two icons may be considered to be crossmodal if and only if they provide a common representation of data, which is accessible interchangeably via different modalities. Two experiments investigated possible parameters for use in crossmodal icons with results showing that rhythm, texture and spatial location are effective. A third experiment focused on learning multi-dimensional crossmodal icons and the extent to which this learning transfers between modalities. The results showed identification rates of 92% for three-dimensional audio crossmodal icons when trained in the tactile equivalents, and identification rates of 89% for tactile crossmodal icons when trained in the audio equivalent. Crossmodal icons were then incorporated into a mobile touchscreen QWERTY keyboard. Experiments showed that keyboards with audio or tactile feedback produce fewer errors and greater speeds of text entry compared to standard touchscreen keyboards. The next study examined how environmental variables affect user performance with the same keyboard. The data showed that each modality performs differently with varying levels of background noise or vibration and the exact levels at which these performance decreases occur were established. The final study involved a longitudinal evaluation of a touchscreen application, CrossTrainer, focusing on longitudinal effects on performance with audio and tactile feedback, the impact of context on performance and personal modality preference. The results show that crossmodal audio and tactile icons are a valid method of presenting information to situationally impaired mobile touchscreen users with recognitions rates of 100% over time. This thesis concludes with a set of guidelines on the design and application of crossmodal audio and tactile feedback to enable application and interface designers to employ such feedback in all systems

Designed with older adults to support better error correction in smartphone text entry : the MaxieKeyboard

Through our participatory design with older adults a need for improved error support for texting on smartphones emerged. Here we present the MaxieKeyboard based on the outcomes from this process. The keyboard highlights errors, auto-corrections and suggestion bar usage in the composition area and gives feedback on the keyboard on typing correctness. Our older adult groups have shown strong support for the keyboard

Typing performance of blind users:an analysis of touch behaviors, learning effect, and in-situ usage

Non-visual text-entry for people with visual impairments has focused mostly on the comparison of input techniques reporting on performance measures, such as accuracy and speed. While researchers have been able to establish that non-visual input is slow and error prone, there is little understanding on how to improve it. To develop a richer characterization of typing performance, we conducted a longitudinal study with five novice blind users. For eight weeks, we collected in-situ usage data and conducted weekly laboratory assessment sessions. This paper presents a thorough analysis of typing performance that goes beyond traditional aggregated measures of text-entry and reports on character-level errors and touch measures. Our findings show that users improve over time, even though it is at a slow rate (0.3 WPM per week). Substitutions are the most common type of error and have a significant impact on entry rates. In addition to text input data, we analyzed touch behaviors, looking at touch contact points, exploration movements, and lift positions. We provide insights on why and how performance improvements and errors occur. Finally, we derive some implications that should inform the design of future virtual keyboards for non-visual input. Categories and Subject Descriptors H.5.2 [Information Interfaces and Presentation]: User Interfaces- Input devices and strategies. K4.2 [Computers an