While a photograph is a visual artifact, studies reveal that a number of people with visual impairments are also interested in being able to share their memories and experiences with their sighted counterparts in the form of a photograph. We conducted an online survey to better understand the challenges faced by people with visual impairments in sharing and organizing photos, and reviewed existing tools and their limitations. Based on our analysis, we developed an accessible mobile application that enables a visually impaired user to capture photos along with audio recordings for the ambient sound and memo description and to browse through them eyes-free. Five visually impaired participants took part in a study in which they used our app to take photographs in naturalistic settings and to share them later with a sighted viewer. The participants were able to use our app to identify each photograph on their own during the photo sharing session, and reported high satisfaction in having been able to take the initiative during the process.

We present and evaluate an approach towards eyes-free auditory display of spatial information that considers radial direction as a fundamental type of value primitive. There are many benefits to being able to sonify radial directions, such as indicating the heading towards a point of interest in a direct and dynamic manner, rendering a path or shape outline by sonifying a continual sequence of tangent directions as the path is traced, and providing direct feedback of the direction of motion of the user in a physical space or a pointer in a virtual space. We propose a concrete mapping of vowel-like sounds to radial directions as one potential method to enable sonification of such information. We conducted a longitudinal study with five sighted and two blind participants to evaluate the learnability and effectiveness of this method. Results suggest that our directional sound mapping can be learned within a few hours and be used to aurally perceive spatial information such as shape outlines and path contours.

We conducted a 2.5 week longitudinal study with five motor impaired (MI) and four non-impaired (NMI) participants, in which they learned to use the Vocal Joystick, a voice-based user interface control system. We found that the participants were able to learn the mapping between the vowel sounds and directions used by the Vocal Joystick, and showed marked improvement in their target acquisition performance. At the end of the ten session period, the NMI group reached the same level of performance as the previously measured "expert" Vocal Joystick performance, and the MI group was able to reach 70% of that level. Two of the MI participants were also able to approach the performance of their preferred device, a touchpad. We report on a number of issues that can inform the development of further enhancements in the realm of voice-driven computer control.

We present a novel method of dynamic C-D gain adaptation that improves target acquisition for users with motor impairments. Our method, called the Angle Mouse, adjusts the mouse C-D gain based on the deviation of angles sampled during movement. When angular deviation is low, the gain is kept high. When angular deviation is high, the gain is dropped, making the target bigger in motor-space. A key feature of the Angle Mouse is that, unlike most pointing facilitation techniques, it is target-agnostic, requiring no knowledge of target locations or dimensions. This means that the problem of distractor targets is avoided because adaptation is based solely on the user's behavior. In a study of 16 people, 8 of which had motor impairments, we found that the Angle Mouse improved motor-impaired pointing throughput by 10.3% over the Windows default mouse and 11.0% over sticky icons. For able-bodied users, there was no significant difference among the three techniques, as Angle Mouse throughput was within 1.2% of the default. Thus, the Angle Mouse improved pointing performance for users with motor impairments while remaining unobtrusive for able-bodied users.

For decades, Fitts' law (1954) has been used to model pointing time in user interfaces. As with any rapid motor act, faster pointing movements result in increased errors. But although prior work has examined accuracy as the "spread of hits," no work has formulated a predictive model for error rates (0-100%) based on Fitts' law parameters. We show that Fitts' law mathematically implies a predictive error rate model, which we derive. We then describe an experiment in which target size, target distance, and movement time are manipulated. Our results show a strong model fit: a regression analysis of observed vs. predicted error rates yields a correlation of R2=.959 for N=90 points. Furthermore, we show that the effect on error rate of target size (W) is greater than that of target distance (A), indicating a departure from Fitts' law, which maintains that W and A contribute proportionally to index of difficulty (ID). Our error model can be used with Fitts' law to estimate and predict error rates along with speeds, providing a framework for unifying this dichotomy.

Many mobile machine learning applications require collecting and labeling data, and a traditional GUI on a mobile device may not be an appropriate or viable method for this task. This paper presents an alternative approach to mobile labeling of sensor data called VoiceLabel. VoiceLabel consists of two components: (1) a speech-based data collection tool for mobile devices, and (2) a desktop tool for offline segmentation of recorded data and recognition of spoken labels. The desktop tool automatically analyzes the audio stream to find and recognize spoken labels, and then presents a multimodal interface for reviewing and correcting data labels using a combination of the audio stream, the system's analysis of that audio, and the corresponding mobile sensor data. A study with ten participants showed that VoiceLabel is a viable method for labeling mobile sensor data. VoiceLabel also illustrates several key features that inform the design of other data labeling tools.

We present VoiceDraw, a voice-driven drawing application for people with motor impairments that provides a way to generate free-form drawings without needing manual interaction. VoiceDraw was designed and built to investigate the potential of the human voice as a modality to bring fluid, continuous direct manipulation interaction to users who lack the use of their hands. VoiceDraw also allows us to study the issues surrounding the design of a user interface optimized for non-speech voice-based interaction. We describe the features of the VoiceDraw application, our design process, including our user-centered design sessions with a 'voice painter', and offer lessons learned that could inform future voice-based design efforts. In particular, we offer insights for mapping human voice to continuous control.

Speech has great potential as an input mechanism for ubiquitous computing. However, the current requirements necessary for accurate speech recognition, such as a quiet environment and a well-positioned and high-quality microphone, are unreasonable to expect in a realistic setting. In a physical environment, there is often contextual information which can be sensed and used to augment the speech signal. We investigated improving speech recognition rates for an electronic personal trainer using knowledge about what equipment was in use as context. We performed an experiment with participants speaking in an instrumented apartment environment and compared the recognition rates of a larger grammar with those of a smaller grammar that is determined by the context. Figure 1: We tagged gym objects with modified RFID tags to provide context to the speech recognizer. Categories and Subject Descriptors the device. Speech itself is commonly used and so requires little H.5.2 [User Interfaces]: Voice I/O additional training to use. However, current speech recognizers are often inaccurate in non-controlled conditions due to ambient General Terms

This paper explores using non-linguistic vocalization as an additional modality to augment digital pen input on a tablet computer. We investigated this through a set of novel interaction techniques and a feasibility study. Typically, digital pen users control one or two parameters using stylus position and sometimes pen pressure. However, in many scenarios the user can benefit from the ability to continuously vary additional parameters. Non-linguistic vocalizations, such as vowel sounds, variation of pitch, or control of loudness have the potential to provide fluid continuous input concurrently with pen interaction. We present a set of interaction techniques that leverage the combination of voice and pen input when performing both creative drawing and object manipulation tasks. Our feasibility evaluation suggests that with little training people can use non-linguistic vocalization to productively augment digital pen interaction.

Mouse control has become a crucial aspect of many modern day computer interactions. This poses a challenge for individuals with motor impairments or those whose use of hands are restricted due to situational constraints. We present a system called the Vocal Joystick which allows the user to continuously control the mouse cursor by varying vocal parameters such as vowel quality, loudness and pitch. A survey of existing cursor control methods is presented to highlight the key characteristics of the Vocal Joystick. Evaluations were conducted to characterize expert performance capability of the Vocal Joystick, and to compare novice user performance and preference for the Vocal Joystick and two other existing speech based cursor control methods. Our results show that Fitts' law is a good predictor of the speedaccuracy tradeoff for the Vocal Joystick, and suggests that the optimal performance of the Vocal Joystick may be comparable to that of a conventional hand-operated joystick. Novice user evaluations show that the Vocal Joystick can be used by people without extensive training, and that it presents a viable alternative to existing speech-based cursor control methods.

The Vocal Joystick is a novel human-computer interface mechanism designed to enable individuals with motor impairments to make use of vocal parameters to control objects on a computer screen (buttons, sliders, etc.) and ultimately electro-mechanical instruments (e.g., robotic arms, wireless home automation devices). We have developed a working prototype of our "VJ-engine" with which individuals can now control computer mouse movement with their voice. The core engine is currently optimized according to a number of criterion. In this paper, we describe the engine system design, engine optimization, and user-interface improvements, and outline some of the signal processing and pattern recognition modules that were successful. Lastly, we present new results comparing the vocal joystick with a state-of-the-art eye tracking pointing device, and show that not only is the Vocal Joystick already competitive, for some tasks it appears to be an improvement.

This paper introduces a novel adaptive direction filtering algorithm in the Vocal Joystick (VJ) setting that utilizes context information and applies real-time inference in a continuous space. The VJ system using this algorithm is endowed with the ability to produce movements in arbitrary directions and the ability to draw smooth curves. This is in contrast to previous VJ settings whereby vowel quality was used to determine mouse movement in only a finite discrete set of directions [1]. Index Terms: computer interface, voice control, adaptive filtering 1.

Given time and location information about digital photographs we can automatically generate an abundance of related contextual metadata, using off-the-shelf and Web-based data sources. Among these are the local daylight status and weather conditions at the time and place a photo was taken. This metadata has the potential of serving as memory cues and filters when browsing photo collections, especially as these collections grow into the tens of thousands and span dozens of years.