The theory of affordances

We present the results of an evaluation of the performance of the Leap Motion Controller with the aid of a professional, high-precision, fast motion tracking system A set of static and dynamic measurements was performed with different numbers of tracking objects and configurations For the static measurements, a plastic arm model simulating a human arm was used A set of 37 reference locations was selected to cover the controller's sensory space For the dynamic measurements, a special V-shaped tool, consisting of two tracking objects maintaining a constant distance between them, was created to simulate two human fingers In the static scenario, the standard deviation was less than 05 mm The linear correlation revealed a significant increase in the standard deviation when moving away from the controller The results of the dynamic scenario revealed the inconsistent performance of the controller, with a significant drop in accuracy for samples taken more than 250 mm above the controller's surface The Leap Motion Controller undoubtedly represents a revolutionary input device for gesture-based human-computer interaction; however, due to its rather limited sensory space and inconsistent sampling frequency, in its current configuration it cannot currently be used as a professional tracking system

An Analysis of the Precision and Reliability of the Leap Motion Sensor and Its Suitability for Static and Dynamic Tracking

A mouse has been one of the most successful user interfaces due to its intuitive use. As more devices are equipped with displays and offer rich options for users to choose from, a traditional mouse that requires a surface to operate is no longer sufficient. While different types of air mice are available in the market, they rely on accelerometers and gyroscopes, which significantly limit the accuracy and ease of use. In this paper, we develop a system that can accurately track hand movement to realize a mouse. A unique advantage of our scheme is that it achieves high tracking accuracy using the existing hardware already available in the mobile devices (e.g., smart phones and smart watches) and equipment to be controlled (e.g., smart TVs). More specifically, our approach sends inaudible sound pulses at a few selected frequencies, and uses the frequency shifts to estimate the speed and distance traveled. We then develop techniques to quickly calibrate the distance between speakers and narrow down the device's initial position using its movement trajectory. Based on the information, we continuously track the device's new position in real time. This is feasible because many devices, such as smart TVs, PCs, and laptops, already have multiple speakers. When only one speaker is available, we can leverage the frequency shift of sound along with the phase of received WiFi signal to enable tracking. Our evaluation and user study demonstrate that our system achieves high tracking accuracy (e.g., median error of around 1.4 cm) and ease of use.

Demo: Turning a Mobile Device into a Mouse in the Air

We studied the memorability of free-form gesture sets for invoking actions. We compared three types of gesture sets: user-defined gesture sets, gesture sets designed by the authors, and random gesture sets in three studies with 33 participants in total. We found that user-defined gestures are easier to remember, both immediately after creation and on the next day (up to a 24% difference in recall rate compared to pre-designed gestures). We also discovered that the differences between gesture sets are mostly due to association errors (rather than gesture form errors), that participants prefer user-defined sets, and that they think user-defined gestures take less time to learn. Finally, we contribute a qualitative analysis of the tradeoffs involved in gesture type selection and share our data and a video corpus of 66 gestures for replicability and further analysis.

/pdf/memorability-of-pre-designed-and-user-defined-gesture-sets-1ol629m9po.pdf

Memorability of pre-designed and user-defined gesture sets

Direct-touch interaction on mobile phones revolves around screens that compete for visual attention with users' real-world tasks and activities. This paper investigates the impact of these situational impairments on touch-screen interaction. We probe several design factors for touch-screen gestures, under various levels of environmental demands on attention, in comparison to the status-quo approach of soft buttons. We find that in the presence of environmental distractions, gestures can offer significant performance gains and reduced attentional load, while performing as well as soft buttons when the user's attention is focused on the phone. In fact, the speed and accuracy of bezel gestures did not appear to be significantly affected by environment, and some gestures could be articulated eyes-free, with one hand. Bezel-initiated gestures offered the fastest performance, and mark-based gestures were the most accurate. Bezel-initiated marks therefore may offer a promising approach for mobile touch-screen interaction that is less demanding of the user's attention.

/pdf/experimental-analysis-of-touch-screen-gesture-designs-in-15q0xf0c8r.pdf

Experimental analysis of touch-screen gesture designs in mobile environments

The Nintendo Wii Remote (Wiimote) has served as an input device in 3D user interfaces (3DUIs) but differs from the general-purpose input hardware typically found in research labs and commercial applications. Despite this, no one has systematically evaluated the device in terms of what it offers 3DUI designers. Experience with the Wiimote indicates that it's an imperfect harbinger of a new class of spatially convenient devices, classified in terms of spatial data, functionality, and commodity design. This tutorial presents techniques for using the Wiimote in 3DUIs. It discusses the device's strengths and how to compensate for its limitations, with implications for future spatially convenient devices.

The Wiimote and Beyond: Spatially Convenient Devices for 3D User Interfaces

GestureBar is a novel, approachable UI for learning gestural interactions that enables a walk-up-and-use experience which is in the same class as standard menu and toolbar interfaces. GestureBar leverages the familiar, clean look of a common toolbar, but in place of executing commands, richly discloses how to execute commands with gestures, through animated images, detail tips and an out-of-document practice area. GestureBar's simple design is also general enough for use with any recognition technique and for integration with standard, non-gestural UI components. We evaluate GestureBar in a formal experiment showing that users can perform complex, ecologically valid tasks in a purely gestural system without training, introduction, or prior gesture experience when using GestureBar, discovering and learning a high percentage of the gestures needed to perform the tasks optimally, and significantly outperforming a state of the art crib sheet. The relative contribution of the major design elements of GestureBar is also explored. A second experiment shows that GestureBar is preferred to a basic crib sheet and two enhanced crib sheet variations.

/pdf/gesturebar-improving-the-approachability-of-gesture-based-1g7nmwqc3q.pdf

GestureBar: improving the approachability of gesture-based interfaces

As low cost commercial video game sensors emerge, realistic full body interactions available in the household can also be utilized to support low cost dismounted Soldier training applications. These sensors, such as the Microsoft Kinect, are designed to work with users directly facing them. However, in an environment designed to train a team, larger spaces are necessary along with the freedom to maneuver and turn in all directions. These interactions are not reliably supported with the standard household video game configuration. In this paper, the use of multiple Kinects configured around a large area is examined, giving multiple Soldiers freedom of mobility and 360 degrees turning while wearing a Head Mounted Display. Skeletal recognition algorithms are shown within the Microsoft Kinect Software Development Kit that can be merged using commercially available tools and advanced fusion algorithms to produce better quality representations of users in the real world within a virtual environment. While one Kinect will often lose tracking of parts of a user, this paper shows that several Kinects coupled with inference algorithms can produce a much better tracked representation as users move around. Furthermore, the use of depth images along with the skeletal representations was examined to optimize fusion algorithms when bandwidth is available. Finally, it is shown how these techniques are capable of taking several skeletal representations in the virtual scene and merging them together to form a virtual representation of a single user. This system expands the viability of low cost commercial solutions to Soldier training in complex virtual environments.

/pdf/multi-kinect-tracking-for-dismounted-soldier-training-draz2qwprw.pdf

Multi-Kinect Tracking for Dismounted Soldier Training

Intelligent Tutoring Systems (ITSs) have been shown to be effective in training a variety of military tasks. However, these systems are often limited to laboratory settings on standard PCs and laptops which focus on exercising cognitive skills (e.g., decision-making and problem solving) and may potentially limit the learning and retention of the dismounted Soldiers and Marines training to master physical tasks. Augmented reality presents the possibility of combining intelligent tutoring with hands-on applications in realistic physical environments. In this paper, we examine the use of an augment-reality based adaptive tutoring system for instruction in the wild, locations where no formal training infrastructure is present, and identify the challenges that arise when developing such a system. We began the transition from desktop tutoring to the wild by exploring an existing real life mockup of a market scene along with low cost commercial-off-the-shelf devices (e.g., HMDs coupled with depth cameras) and a 3D model of the environment. The goal of our canning approach is to use “human in the loop” 3D scene acquisition via augmented reality so that the scene can be scanned efficiently and with complete coverage. Using this 3D model, intelligent tutoring systems can adaptively manage instruction while being aware of the physical and augmented objects in the scenario. Furthermore, with this awareness of the physical environment, we hope to provide augmented effects and objects (e.g., virtual humans) that register to the physical environment and respond realistically to interactions with the trainee. We also explored developing a training scenario for evaluation of our system that is made to work with emerging low-cost commercial augmented reality devices (e.g., Epson Moverio). Our approach examines the merging of intelligent tutoring with augmented reality to be used for hands-on immersive training of psychomotor tasks in a setting beyond the typical desktop tutoring session.

/pdf/using-augmented-reality-to-tutor-military-tasks-in-the-wild-588zvab2iv.pdf

Using Augmented Reality to Tutor Military Tasks in the Wild

We present a reality-based locomotion study directly applicable to video game interfaces; specifically, locomotion control of the quarterback in American football. Focusing on American football drives requirements and ecologically grounds the interface tasks of: running down the field, maneuvering in a small area, and evasive gestures such as spinning, jumping, and the “juke.”

/pdf/realnav-exploring-natural-user-interfaces-for-locomotion-in-3lwbi0gign.pdf

RealNav: Exploring natural user interfaces for locomotion in video games

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