Comparing Evaluation Methods for Encumbrance and Walking on Interaction with Touchscreen Mobile Devices

In this paper, two walking evaluation methods were compared to evaluate the effects of encumbrance while the preferred walking speed (PWS) is controlled. Users frequently carry cumbersome objects (e.g. shopping bags) and use mobile devices at the same time which can cause interaction difficulties and erroneous input. The two methods used to control the PWS were: walking on a treadmill and walking around a predefined route on the ground while following a pacesetter. The results from our target acquisition experiment showed that for ground walking at 100% of PWS, accuracy dropped to 36% when carrying a bag in the dominant hand while accuracy reduced to 34% for holding a box under the dominant arm. We also discuss the advantages and limitations of each evaluation method when examining encumbrance and suggest treadmill walking is not the most suitable approach to use if walking speed is an important factor in future mobile studies

Investigating Performance and Usage of Input Methods for Soft Keyboard Hotkeys

Touch-based devices, despite their mainstream availability, do not support a unified and efficient command selection mechanism, available on every platform and application. We advocate that hotkeys, conventionally used as a shortcut mechanism on desktop computers, could be generalized as a command selection mechanism for touch-based devices, even for keyboard-less applications. In this paper, we investigate the performance and usage of soft keyboard shortcuts or hotkeys (abbreviated SoftCuts) through two studies comparing different input methods across sitting, standing and walking conditions. Our results suggest that SoftCuts not only are appreciated by participants but also support rapid command selection with different devices and hand configurations. We also did not find evidence that walking deters their performance when using the Once input method.Comment: 17+2 pages, published at Mobile HCI 202

Ambient Gestures

We present Ambient Gestures, a novel gesture-based system designed to support ubiquitous ‘in the environment’ interactions with everyday computing technology. Hand gestures and audio feedback allow users to control computer applications without reliance on a graphical user interface, and without having to switch from the context of a non-computer task to the context of the computer. The Ambient Gestures system is composed of a vision recognition software application, a set of gestures to be processed by a scripting application and a navigation and selection application that is controlled by the gestures. This system allows us to explore gestures as the primary means of interaction within a multimodal, multimedia environment. In this paper we describe the Ambient Gestures system, define the gestures and the interactions that can be achieved in this environment and present a formative study of the system. We conclude with a discussion of our findings and future applications of Ambient Gestures in ubiquitous computing

Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)

Design and Evaluation of Menu Systems for Immersive Virtual Environments

Interfaces for system control tasks in virtual environments (VEs) have not been extensively studied. This paper focuses on various types of menu systems to be used in such environments. We describe the design of the TULIP menu, a menu system using Pinch Gloves™, and compare it to two common alternatives: floating menus and pen and tablet menus. These three menus were compared in an empirical evaluation. The pen and tablet menu was found to be significantly faster, while users had a preference for TULIP. Subjective discomfort levels were also higher with the floating menus and pen and tablet

Nomadic input on mobile devices: the influence of touch input technique and walking speed on performance and offset modeling

In everyday life people use their mobile phones on-the-go with different walking speeds and with different touch input techniques. Unfortunately, much of the published research in mobile interaction does not quantify the influence of these variables. In this paper, we analyze the influence of walking speed, gait pattern and input techniques on commonly used performance parameters like error rate, accuracy and tapping speed, and we compare the results to the static condition. We examine the influence of these factors on the machine learned offset model used to correct user input and we make design recommendations. The results show that all performance parameters degraded when the subject started to move, for all input techniques. Index finger pointing techniques demonstrated overall better performance compared to thumb-pointing techniques. The influence of gait phase on tap event likelihood and accuracy was demonstrated for all input techniques and all walking speeds. Finally, it was shown that the offset model built on static data did not perform as well as models inferred from dynamic data, which indicates the speed-specific nature of the models. Also, models identified using specific input techniques did not perform well when tested in other conditions, demonstrating the limited validity of offset models to a particular input technique. The model was therefore calibrated using data recorded with the appropriate input technique, at 75% of preferred walking speed, which is the speed to which users spontaneously slow down when they use a mobile device and which presents a tradeoff between accuracy and usability. This led to an increase in accuracy compared to models built on static data. The error rate was reduced between 0.05% and 5.3% for landscape-based methods and between 5.3% and 11.9% for portrait-based methods

Exploring the Front Touch Interface for Virtual Reality Headsets

In this paper, we propose a new interface for virtual reality headset: a touchpad in front of the headset. To demonstrate the feasibility of the front touch interface, we built a prototype device, explored VR UI design space expansion, and performed various user studies. We started with preliminary tests to see how intuitively and accurately people can interact with the front touchpad. Then, we further experimented various user interfaces such as a binary selection, a typical menu layout, and a keyboard. Two-Finger and Drag-n-Tap were also explored to find the appropriate selection technique. As a low-cost, light-weight, and in low power budget technology, a touch sensor can make an ideal interface for mobile headset. Also, front touch area can be large enough to allow wide range of interaction types such as multi-finger interactions. With this novel front touch interface, we paved a way to new virtual reality interaction methods

An Introduction to 3D User Interface Design

3D user interface design is a critical component of any virtual environment (VE) application. In this paper, we present a broad overview of three-dimensional (3D) interaction and user interfaces. We discuss the effect of common VE hardware devices on user interaction, as well as interaction techniques for generic 3D tasks and the use of traditional two-dimensional interaction styles in 3D environments. We divide most user interaction tasks into three categories: navigation, selection/manipulation, and system control. Throughout the paper, our focus is on presenting not only the available techniques, but also practical guidelines for 3D interaction design and widely held myths. Finally, we briefly discuss two approaches to 3D interaction design, and some example applications with complex 3D interaction requirements. We also present an annotated online bibliography as a reference companion to this article

Multi-touch 3D Exploratory Analysis of Ocean Flow Models

Modern ocean flow simulations are generating increasingly complex, multi-layer 3D ocean flow models. However, most researchers are still using traditional 2D visualizations to visualize these models one slice at a time. Properly designed 3D visualization tools can be highly effective for revealing the complex, dynamic flow patterns and structures present in these models. However, the transition from visualizing ocean flow patterns in 2D to 3D presents many challenges, including occlusion and depth ambiguity. Further complications arise from the interaction methods required to navigate, explore, and interact with these 3D datasets. We present a system that employs a combination of stereoscopic rendering, to best reveal and illustrate 3D structures and patterns, and multi-touch interaction, to allow for natural and efficient navigation and manipulation within the 3D environment. Exploratory visual analysis is facilitated through the use of a highly-interactive toolset which leverages a smart particle system. Multi-touch gestures allow users to quickly position dye emitting tools within the 3D model. Finally, we illustrate the potential applications of our system through examples of real world significance

Multistatic human micro-Doppler classification of armed/unarmed personnel

Classification of different human activities using multistatic micro-Doppler data and features is considered in this paper, focusing on the distinction between unarmed and potentially armed personnel. A database of real radar data with more than 550 recordings from 7 different human subjects has been collected in a series of experiments in the field with a multistatic radar system. Four key features were extracted from the micro-Doppler signature after Short Time Fourier Transform analysis. The resulting feature vectors were then used as individual, pairs, triplets, and all together before inputting to different types of classifiers based on the discriminant analysis method. The performance of different classifiers and different feature combinations is discussed aiming at identifying the most appropriate features for the unarmed vs armed personnel classification, as well as the benefit of combining multistatic data rather than using monostatic data only